In today’s digital landscape, businesses face increasing pressure to maintain service continuity and data availability, especially in the face of potential service disruptions or disasters. Traditional single-cloud deployments can be vulnerable to outages, data loss, and geographical limitations. As a result, multi-cloud strategies have emerged as a solution for achieving higher resilience, disaster recovery (DR), and business continuity. By leveraging multiple cloud service providers (CSPs), organizations can architect (...) redundancy, improve service availability, and ensure faster recovery in the event of a failure. This paper explores the importance of multi-cloud strategies for resilience and disaster recovery, focusing on key components like data replication, geographic distribution, failover mechanisms, and backup strategies. We discuss architectural patterns, best practices, and challenges of implementing multi-cloud disaster recovery solutions. Additionally, we present case studies that highlight how multi-cloud strategies enhance business resilience and improve operational continuity during disruptions. (shrink)

Property and Casualty (P&C) insurers are adopting multi-cloud environments as a strategic imperative because of their increasing data volumes and complexities in regulatory compliance, customer expectations and technological advancements. This paper discusses how Snowflake’s cloud-agnostic, unified platform enables insurers to create resilient, efficient, and compliant multi cloud data strategies. Using Snowflake’s elastic scalability, real-time analytics, secure data sharing and seamless cloud interoperability, insurers can optimize claims processing, augment fraud detection, and support (...) customer engagement. The study offers core design principles for multi cloud data architectures and sheds light on problems in data fragmentation, latency, and security risks. It points to how Snowflake enables data resilience, including data replication, encryption, GDPR, and CCPA compliance. Details are provided. Practical implementation strategies of phased data migration ETL pipelines and integration with existing enterprise system to ensure a smooth deployment. Snowflake is seen as a clear win through a cost-benefit analysis with metrics like RTO and RPO that define how well Snowflake performs disaster recovery and business continuity. Additionally, the paper also looks at the emergence of tech like AI, blockchain and edge computing which could additionally bolster multi-cloud strategies even further. Finally, Snowflake enables the journey for P&C insurers through cloud ecosystem pitfalls, enabling operational resilience, regulatory compliance, along competitive advantage in a data-driven industry. (shrink)

Through a combination of Guidewire platforms, cloud computing and cybersecurity frameworks, the insurance industry in miniature is being transformed into a digitally transformed, ever resilient ecosystem. This ecosystem enables insurers to modernize core operations of policy management, claims processing and billing while continuing to provide secure, scalable and efficient service delivery. Insurers using cloud infrastructure have elastic resources capable of scaling to meet dynamic workloads and can provide high availability and fast disaster recovery. They have (...) liberated data analytics to run on the cloud with advanced capabilities to churn out actionable insights to optimize operations and create personalized offerings for their customers. Sensitive customer data and important systems are securely protected from cyber security threats by a robust cybersecurity layer, which includes data encryption, controls access, and detects threats proactively. Integration and cloud adoption challenges like costly implementation, cybersecurity risk and legacy system migration will be handled by deft and effective strategies such as phased adoption, workforce upskilling and adoption of a zero-trust security model. Within operational efficiency, stronger resilience and innovative business models enabled, i.e. behavior-based insurance pricing through IoT and real time analytics, are the opportunities. However, moisture and regulatory complexity can be barriers to change, and investments in modern technology, workforce development, and partnerships with insurtech and cybersecurity experts can enable insurers to survive and thrive in a more competitive, digital-first landscape. This convergence engenders customer trust, unlocks its operational agility and ensures compliance with changing requirements, thereby enabling insurers to sustain their growth, build resilience and rival digital transformation. (shrink)

As organizations increasingly adopt cloud computing to enhance scalability, efficiency, and costeffectiveness, securing cloud-based applications and data storage has become a paramount concern. This shift has redefined the role of network engineers, who are now at the forefront of implementing and managing secure cloud infrastructures. This research paper examines the critical responsibilities of network engineers in safeguarding cloud environments, focusing on the challenges, strategies, and tools they employ to mitigate risks and ensure data integrity. (...) The paper identifies key challenges associated with cloud security, including data breaches, misconfigured systems, insecure interfaces, and insider threats. These vulnerabilities are further exacerbated by the dynamic and decentralized nature of cloud ecosystems, which often involve multi-tenant architectures and complex integrations. The study underscores the need for network engineers to adapt to these complexities by acquiring specialized skills in cloud security protocols and emerging technologies. A significant focus is placed on the implementation of robust network security frameworks, such as zero-trust architecture, which emphasizes the principle of "never trust, always verify." Network engineers play a crucial role in deploying access control measures, encryption mechanisms, and intrusion detection systems to safeguard sensitive data. Additionally, the research highlights the importance of continuous monitoring and threat intelligence to identify and address vulnerabilities proactively. The integration of automation and artificial intelligence (AI) in cloud security is another critical area explored in this study. Automation tools enable network engineers to streamline routine tasks, such as patch management and configuration audits, thereby reducing human error and response time. AI-driven solutions, on the other hand, enhance threat detection capabilities by analyzing large datasets for anomalous patterns and potential attacks. The paper also discusses the collaborative responsibilities of network engineers, who must work closely with developers, cloud providers, and organizational stakeholders to align security measures with business objectives. This includes implementing secure DevOps practices, ensuring compliance with industry regulations, and conducting regular security audits. Case studies of successful cloud security implementations are presented to illustrate the effectiveness of these collaborative approaches. In conclusion, this research emphasizes the evolving role of network engineers as pivotal defenders of cloud-based systems. It calls for continuous professional development to stay abreast of emerging threats and technologies, as well as fostering a culture of shared responsibility for security within organizations. By adopting a proactive and comprehensive approach, network engineers can significantly enhance the resilience of cloud infrastructures, ensuring the confidentiality, integrity, and availability of critical data and applications. This study aims to provide actionable insights and a strategic framework for network engineers and organizations striving to navigate the complexities of securing cloud environments. Through a combination of technological expertise, innovative tools, and collaborative practices, network engineers are positioned to address the unique challenges posed by cloud computing and play a central role in shaping the future of secure digital ecosystems. (shrink)

As organizations increasingly adopt cloud computing to enhance scalability, efficiency, and costeffectiveness, securing cloud-based applications and data storage has become a paramount concern. This shift has redefined the role of network engineers, who are now at the forefront of implementing and managing secure cloud infrastructures. This research paper examines the critical responsibilities of network engineers in safeguarding cloud environments, focusing on the challenges, strategies, and tools they employ to mitigate risks and ensure data integrity. (...) The paper identifies key challenges associated with cloud security, including data breaches, misconfigured systems, insecure interfaces, and insider threats. These vulnerabilities are further exacerbated by the dynamic and decentralized nature of cloud ecosystems, which often involve multi-tenant architectures and complex integrations. The study underscores the need for network engineers to adapt to these complexities by acquiring specialized skills in cloud security protocols and emerging technologies. A significant focus is placed on the implementation of robust network security frameworks, such as zero-trust architecture, which emphasizes the principle of "never trust, always verify." Network engineers play a crucial role in deploying access control measures, encryption mechanisms, and intrusion detection systems to safeguard sensitive data. Additionally, the research highlights the importance of continuous monitoring and threat intelligence to identify and address vulnerabilities proactively. The integration of automation and artificial intelligence (AI) in cloud security is another critical area explored in this study. Automation tools enable network engineers to streamline routine tasks, such as patch management and configuration audits, thereby reducing human error and response time. AI-driven solutions, on the other hand, enhance threat detection capabilities by analyzing large datasets for anomalous patterns and potential attacks. The paper also discusses the collaborative responsibilities of network engineers, who must work closely with developers, cloud providers, and organizational stakeholders to align security measures with business objectives. This includes implementing secure DevOps practices, ensuring compliance with industry regulations, and conducting regular security audits. Case studies of successful cloud security implementations are presented to illustrate the effectiveness of these collaborative approaches. In conclusion, this research emphasizes the evolving role of network engineers as pivotal defenders of cloud-based systems. It calls for continuous professional development to stay abreast of emerging threats and technologies, as well as fostering a culture of shared responsibility for security within organizations. By adopting a proactive and comprehensive approach, network engineers can significantly enhance the resilience of cloud infrastructures, ensuring the confidentiality, integrity, and availability of critical data and applications. This study aims to provide actionable insights and a strategic framework for network engineers and organizations striving to navigate the complexities of securing cloud environments. Through a combination of technological expertise, innovative tools, and collaborative practices, network engineers are positioned to address the unique challenges posed by cloud computing and play a central role in shaping the future of secure digital ecosystems. (shrink)

The adoption of multi-cloud environments has become a strategic necessity for organizations seeking scalability, flexibility, and operational efficiency. However, distributing workloads across multiple cloud providers introduces significant security challenges, including authentication vulnerabilities, inconsistent security policies, data breaches, and compliance risks. Traditional security approaches often fail to address the complexity of multi-cloud ecosystems, requiring a more comprehensive risk mitigation strategy. This paper analyses key security risks in multi-cloud architectures and evaluates industry-standard risk assessment (...) frameworks to prioritize effective countermeasures. Our findings indicate that authentication, access control, and secure cloud networking are the most critical areas requiring immediate attention. Threats such as identity mismanagement, insecure data transfers, and lack of unified monitoring further escalate security concerns. To mitigate these risks, we propose a combination of zero-trust architecture, robust identity and access management (IAM), encryption protocols, and AI-driven threat detection. Implementing these strategies can enhance data integrity, regulatory compliance, and overall cloud security posture. By adopting a proactive approach, IT leaders can optimize cybersecurity investments and ensure the resilience of multi-cloud environments. This study provides actionable insights to strengthen security in distributed cloud architectures, enabling organizations to defend against evolving cyber threats. (shrink)

The adoption of multi-cloud environments has become a strategic necessity for organizations seeking scalability, flexibility, and operational efficiency. However, distributing workloads across multiple cloud providers introduces significant security challenges, including authentication vulnerabilities, inconsistent security policies, data breaches, and compliance risks. Traditional security approaches often fail to address the complexity of multi-cloud ecosystems, requiring a more comprehensive risk mitigation strategy. This paper analyses key security risks in multi-cloud architectures and evaluates industry-standard risk assessment (...) frameworks to prioritize effective countermeasures. Our findings indicate that authentication, access control, and secure cloud networking are the most critical areas requiring immediate attention. Threats such as identity mismanagement, insecure data transfers, and lack of unified monitoring further escalate security concerns. To mitigate these risks, we propose a combination of zero-trust architecture, robust identity and access management (IAM), encryption protocols, and AI-driven threat detection. Implementing these strategies can enhance data integrity, regulatory compliance, and overall cloud security posture. By adopting a proactive approach, IT leaders can optimize cybersecurity investments and ensure the resilience of multi-cloud environments. This study provides actionable insights to strengthen security in distributed cloud architectures, enabling organizations to defend against evolving cyber threats. (shrink)

As businesses increasingly migrate to cloud environments, the need for robust and adaptive cloud security frameworks becomes paramount. Cloud services provide numerous benefits such as scalability, flexibility, and cost-efficiency, but they also introduce significant risks in terms of privacy, compliance, and data protection. This paper explores the evolving landscape of cloud security, focusing on next-generation frameworks that aim to balance the often-competing demands of privacy, regulatory compliance, and data protection. We analyze emerging security models (...) that incorporate advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Zero Trust Architecture (ZTA), and Blockchain, all of which are reshaping cloud security. Additionally, we discuss the challenges organizations face in managing security across multi-cloud and hybrid environments, and the role of cloud service providers in ensuring compliance with global regulations like GDPR, HIPAA, and CCPA. The paper concludes by outlining best practices and strategies that organizations can adopt to enhance their cloud security posture and ensure the privacy and protection of sensitive data. (shrink)

As organizations increasingly adopt multi-cloud and hybrid cloud strategies, the need for efficient management of resources across diverse cloud environments becomes more complex. Azure Arc, a key offering from Microsoft, enables organizations to extend Azure services and management to any infrastructure, whether on-premises, in other clouds, or at the edge. By offering a unified platform for managing hybrid and multi-cloud environments, Azure Arc enhances governance, security, and resource optimization across various infrastructures. This paper explores (...) how Azure Arc optimizes hybrid cloud architectures by enabling seamless management, enhanced scalability, and better compliance across different cloud platforms. It also discusses key use cases, benefits, and challenges of adopting Azure Arc, with practical guidance on how to leverage this solution to streamline operations and improve cost efficiency in a multi-cloud ecosystem. The paper aims to provide IT leaders with actionable insights for deploying Azure Arc to create a resilient, flexible, and optimized hybrid cloud environment. (shrink)

Cloud computing has transformed how businesses design, deploy, and manage applications. With its ability to provide on-demand resources and scalability, cloud platforms offer significant benefits in terms of flexibility, cost-efficiency, and speed. However, to fully leverage these advantages, it is crucial to apply proven design patterns that ensure the cloud architecture is both scalable and secure. This paper explores key cloud architecture design patterns, including microservices, serverless architecture, multi-cloud, and hybrid cloud, along with (...) best practices for achieving scalable and secure systems. Emphasizing security and scalability, it provides guidance for organizations on how to build resilient and high-performing cloud systems. (shrink)

: In the digital age, cybersecurity and network engineering are two integral disciplines that must operate in unison to safeguard information and ensure the resilience of modern systems. While network engineering focuses on designing, implementing, and maintaining infrastructure, cybersecurity emphasizes protecting this infrastructure and its data from threats. This research explores the intricate relationship between these domains, advocating for a unified approach to achieve optimal protection in an increasingly interconnected world. The study highlights the evolving threat landscape characterized by (...) sophisticated cyberattacks, including ransomware, distributed denial-of-service (DDoS) attacks, and advanced persistent threats (APTs). Traditional security measures often fail to address the dynamic nature of these threats, necessitating a deeper integration of cybersecurity principles into network design. By embedding security at every layer of the network architecture, organizations can create systems that are not only efficient but also inherently resilient to attacks. Central to this paper is the concept of security-by-design, a methodology that integrates cybersecurity considerations into the initial stages of network engineering. This approach ensures that vulnerabilities are minimized from the outset rather than being addressed reactively. Techniques such as zero-trust architecture, micro-segmentation, and software-defined networking (SDN) are discussed as pivotal strategies for achieving a secure network environment. Additionally, the role of artificial intelligence (AI) and machine learning (ML) in detecting anomalies and predicting potential breaches is examined, underscoring the importance of innovation in maintaining robust cybersecurity defenses. The research also explores the human factor, emphasizing the need for cross-disciplinary training that equips network engineers with cybersecurity expertise and vice versa. Collaborative frameworks, such as DevSecOps, are presented as effective means of fostering communication and cooperation between teams. These frameworks not only streamline operations but also enhance the overall security posture of organizations. Case studies from sectors such as healthcare, finance, and critical infrastructure provide practical insights into the successful integration of cybersecurity and network engineering. These examples illustrate how proactive measures have mitigated risks, minimized downtime, and ensured business continuity. However, the study also identifies challenges, including the high cost of implementation, skill shortages, and resistance to change within organizations. This research concludes that bridging the gap between cybersecurity and network engineering is no longer optional but a necessity in an era of relentless cyber threats. By adopting a holistic approach that prioritizes collaboration, innovation, and adaptability, organizations can build networks that are both secure and efficient. Future work should focus on developing standardized protocols and frameworks that facilitate seamless integration and address emerging challenges. This researchcontributes to the broader discourse on cybersecurity and network engineering by providing actionable insights and advocating for a paradigm shift that aligns these disciplines for optimal protection. The findings serve as a call to action for stakeholders to embrace a collaborative mindset, ensuring that the digital infrastructure of tomorrow is resilient, adaptive, and secure. (shrink)

This work introduces the framework for selecting architecture in 5G networks, considering various technological, performance, economic, and operational factors. With the emergence of 5G technology, the architecture selection process has become pivotal in meeting diverse requirements for ultra-high-speed connectivity, low latency, scalability, and diverse service demands. The evaluation comprehensively analyses different architecture options, including centralized, distributed, cloud-based, and virtualized architectures. Factors such as network performance, scalability, cost-effectiveness, security, and compatibility are considered within a multi-criteria decision-making framework. (...) Findings reveal each architecture option's diverse strengths and limitations, emphasizing the importance of aligning architectural choices with specific use cases, deployment scenarios, and business objectives. We used the concept of multi-criteria decision-making (MCDM) methodology to control the various criteria. We used the single-valued neutrosophic set (SVNS) to deal with vague and inconsistent data. The SVNS is integrated with the hyperSoft set. The proposed methodology uses a single-valued neutrosophic hyperSoft set (SVNHSS) to select the best 5G architecture. We used the VIKOR method as an MCDM method to rank the alternatives. The proposed framework provides stakeholders with a structured methodology to evaluate and prioritize architecture options, facilitating informed decision-making in the complex landscape of 5G network deployments. (shrink)

As cloud-native architectures continue to evolve, microservices have become the foundation for scalable and resilient applications. However, the decentralized nature of microservices introduces significant security challenges, including service-to-service communication security, identity management, and traffic control. Service mesh technologies, such as Istio, Linkerd, and Consul, provide a powerful solution by offering decentralized security enforcement, mutual TLS (mTLS) encryption, fine-grained access control, and observability without modifying application code. This paper explores how service meshes enhance microservices security by (...) implementing zero-trust policies, automatic traffic encryption, and service authentication mechanisms. Through the integration of policy-based access control, workload identity, and anomaly detection, service mesh architectures significantly reduce the risk of unauthorized access, lateral movement attacks, and data breaches. Additionally, this study highlights best practices for deploying secure service meshes in cloud-native environments, ensuring compliance with industry security standards. The findings demonstrate that adopting a service mesh improves security posture while maintaining agility and performance in microservices-based applications. (shrink)

Cloud computing has transformed the nature of computation, sharing of information resources, and storage capabilities, including the flexibility to scale these resources for corporate use. Nevertheless, maintaining high reliability in cloud environments is still an issue that has not been solved because of factors such as Hardware failures, network interruptions/slowdowns and software vulnerabilities. This paper discusses several methods that can be employed in the reliability engineering of cloud computing, including fault tolerance, redundancy, monitoring and predictive maintenance. (...) It also further extends the basic reliability measures such as Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), Service Availability and Failure Rate, which measure system reliability and effectiveness. Moreover, the paper considers performance assessment methodologies through real-time monitoring, machine learning, and reliability assessment methods. It also addresses the nature and advancement of technologies of artificial intelligence-powered automation and self-healing applications for improved cloud dependability. The present work aims to identify the state-of-the-art state of dependability in cloud services and propose some recommendations for minimizing such costs, improving dependability levels, and reducing undesired downtime. The information is valuable for CSPs, IT designers/architects, and system engineers who wish to create fault-tolerant and optimal cloud environments. (shrink)

Containerization in a multi-cloud environment facilitates workload portability and optimized resource uti-lization. Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of containerization in multi-cloud environment. We conducted the Systematic Mapping Study (SMS) on the literature (...) published between January 2013 and March 2023. Eighty-six studies were finally selected and the key results are: (1) Four leading themes on cloud computing and network systems research were identified: ‘Scalability and High Availability’, ‘Performance and Optimization’, ‘Security and Privacy’, and ‘Multi-Cloud Container Monitoring and Adaptation’. (2) Seventy-four patterns and strategies for containerization in multi-cloud environment were classified across 10 subcategories and 4 categories. (3) Ten quality attributes considered were identified with 47 associated tactics. (4) Four frameworks were introduced from the analysis of identified challenges and solutions: security, automation, deployment, and monitoring challenge-solution frameworks. The results of this SMS will assist researchers and practitioners in pursuing further studies on containerization in multi-cloud environment and developing specialized solutions for containerization applications in multi-cloud environment. (shrink)

The rise of remote work has redefined the modern workplace, driven by advancements in technology and the global need for flexible work environments. While remote work offers increased productivity and employee satisfaction, it has also introduced a host of network security challenges. Organizations must contend with securing sensitive data and critical applications across distributed networks, many of which extend beyond traditional corporate perimeters. This research paper explores the multifaceted security threats associated with remote work environments, including phishing attacks, unpatched (...) software vulnerabilities, unsecured home networks, and the misuse of personal devices. These challenges are exacerbated by a lack of centralized control and the increasing sophistication of cybercriminals exploiting these vulnerabilities. Key findings highlight the importance of robust authentication protocols, such as multi-factor authentication (MFA), to ensure secure access to corporate resources. Endpoint security solutions, including encryption and antivirus software, play a critical role in safeguarding data on remote devices. Moreover, the paper emphasizes the significance of implementing virtual private networks (VPNs) and zero-trust security frameworks to reduce attack surfaces and limit unauthorized access. Another critical focus is the role of employee training and awareness programs in mitigating human error, which remains a leading cause of data breaches. This study also underscores the necessity of proactive threat detection and response mechanisms to identify and address breaches in real-time. Advanced technologies, such as artificial intelligence (AI) and machine learning (ML), are pivotal in enhancing these capabilities, enabling organizations to adapt to the rapidly evolving cyber threat landscape. Furthermore, comprehensive network segmentation strategies are discussed to isolate critical systems and minimize the impact of potential breaches. The research incorporates case studies of successful implementations of these solutions, demonstrating their effectiveness in fortifying network security for remote operations. A comparative analysis is presented to evaluate the cost-effectiveness and scalability of various approaches, catering to organizations of different sizes and industries. The findings suggest that a multi-layered defense strategy is essential for achieving resilience in remote work environments. The paper concludes with actionable recommendations for organizations seeking to enhance their security posture. These include prioritizing regular updates and patches, adopting secure cloud-based collaboration tools, and conducting periodic security audits. Additionally, fostering a culture of cybersecurity awareness among employees is paramount to addressing the human factor in cyber risks. By addressing the unique challenges posed by remote work, this study provides a comprehensive roadmap for protecting data and applications in decentralized environments. The insights gained aim to equip organizations with the tools and knowledge necessary to navigate the complexities of securing remote work infrastructures while maintaining business continuity and trust. (shrink)

As businesses and individuals increasingly rely on cloud computing for a vast array of services, the need for robust cloud security becomes ever more critical. Cloud environments offer unparalleled flexibility, scalability, and cost-effectiveness but simultaneously introduce a complex security landscape that poses significant challenges. The importance of safeguarding data, applications, and systems from cyber threats in the cloud is paramount to maintaining the confidentiality, integrity, and availability of digital assets. This paper explores the evolving landscape (...) of cloud security, identifying the risks and vulnerabilities inherent in cloud computing and the key strategies and technologies used to mitigate them. We introduce a comprehensive framework for cloud security that incorporates encryption, access management, threat detection, and compliance adherence. Emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and blockchain are also discussed, showcasing their role in transforming cloud security practices. By analyzing best practices and future trends, this paper provides a roadmap for securing the digital frontier in an era increasingly defined by cloud computing. (shrink)

: As cloud-native architectures continue to evolve, microservices have become the foundation for scalable and resilient applications. However, the decentralized nature of microservices introduces significant security challenges, including service-to-service communication security, identity management, and traffic control. Service mesh technologies, such as Istio, Linkerd, and Consul, provide a powerful solution by offering decentralized security enforcement, mutual TLS (mTLS) encryption, fine-grained access control, and observability without modifying application code. This paper explores how service meshes enhance microservices security (...) by implementing zero-trust policies, automatic traffic encryption, and service authentication mechanisms. Through the integration of policy-based access control, workload identity, and anomaly detection, service mesh architectures significantly reduce the risk of unauthorized access, lateral movement attacks, and data breaches. Additionally, this study highlights best practices for deploying secure service meshes in cloud-native environments, ensuring compliance with industry security standards. The findings demonstrate that adopting a service mesh improves security posture while maintaining agility and performance in microservices-based applications. (shrink)

In the modern enterprise environment, where cybersecurity threats continue to evolve in complexity and sophistication, network segmentation and micro-segmentation have emerged as critical strategies for mitigating risks and reducing attack surfaces. This research paper explores the principles, implementation, and benefits of network segmentation and micro-segmentation as essential components of a comprehensive cybersecurity framework. By dividing networks into smaller, isolated segments, these methodologies aim to limit unauthorized access, minimize lateral movement, and contain potential breaches, ensuring a more secure network infrastructure. Network (...) segmentation focuses on dividing large networks into smaller, more manageable subnetworks. This process enforces boundaries between different areas of a network, reducing exposure and protecting sensitive data. Meanwhile, microsegmentation extends this concept to the individual workload level, offering granular security controls that adapt to dynamic and cloud-based environments. These approaches are particularly relevant in today's context, where hybrid infrastructures and multi-cloud deployments are becoming the norm, posing significant security challenges. The paper examines the technical underpinnings of segmentation techniques, highlighting tools and frameworks that facilitate their deployment. It also addresses key challenges, such as the complexity of configuration, potential performance bottlenecks, and the necessity for alignment with broader organizational policies. Case studies from industries such as healthcare, finance, and government are analyzed to demonstrate the effectiveness of segmentation in reducing the scope and impact of cyberattacks. Additionally, this study delves into the evolving landscape of cyber threats, emphasizing the role of segmentation in countering advanced persistent threats (APTs), ransomware attacks, and insider threats. By adopting a zero-trust architecture that integrates micro-segmentation, organizations can ensure that every access request is verified and confined to the least privileged level necessary. This proactive approach to network defense aligns with industry best practices and regulatory standards, enhancing an organization's security posture. Furthermore, the research highlights the importance of continuous monitoring and automation in maintaining segmented networks. Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are explored for their potential to optimize and simplify segmentation processes. These advancements enable organizations to dynamically adapt to evolving threats while maintaining operational efficiency. The findings emphasize that while network segmentation and micro-segmentation are not silver bullets, they represent indispensable layers of defense within a multi-faceted cybersecurity strategy. Organizations that successfully implement these strategies can significantly reduce the likelihood and impact of breaches, protect critical assets, and build resilience against future threats. This paper aims to provide a comprehensive guide for cybersecurity professionals, IT administrators, and policymakers to understand and adopt network segmentation and micro-segmentation. By integrating these strategies into their security frameworks, enterprises can fortify their defenses in the face of a constantly shifting threat landscape, safeguarding their infrastructure, data, and operations. (shrink)

In the modern enterprise environment, where cybersecurity threats continue to evolve in complexity and sophistication, network segmentation and micro-segmentation have emerged as critical strategies for mitigating risks and reducing attack surfaces. This research paper explores the principles, implementation, and benefits of network segmentation and micro-segmentation as essential components of a comprehensive cybersecurity framework. By dividing networks into smaller, isolated segments, these methodologies aim to limit unauthorized access, minimize lateral movement, and contain potential breaches, ensuring a more secure network infrastructure. Network (...) segmentation focuses on dividing large networks into smaller, more manageable subnetworks. This process enforces boundaries between different areas of a network, reducing exposure and protecting sensitive data. Meanwhile, microsegmentation extends this concept to the individual workload level, offering granular security controls that adapt to dynamic and cloud-based environments. These approaches are particularly relevant in today's context, where hybrid infrastructures and multi-cloud deployments are becoming the norm, posing significant security challenges. The paper examines the technical underpinnings of segmentation techniques, highlighting tools and frameworks that facilitate their deployment. It also addresses key challenges, such as the complexity of configuration, potential performance bottlenecks, and the necessity for alignment with broader organizational policies. Case studies from industries such as healthcare, finance, and government are analyzed to demonstrate the effectiveness of segmentation in reducing the scope and impact of cyberattacks. Additionally, this study delves into the evolving landscape of cyber threats, emphasizing the role of segmentation in countering advanced persistent threats (APTs), ransomware attacks, and insider threats. By adopting a zero-trust architecture that integrates micro-segmentation, organizations can ensure that every access request is verified and confined to the least privileged level necessary. This proactive approach to network defense aligns with industry best practices and regulatory standards, enhancing an organization's security posture. Furthermore, the research highlights the importance of continuous monitoring and automation in maintaining segmented networks. Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are explored for their potential to optimize and simplify segmentation processes. These advancements enable organizations to dynamically adapt to evolving threats while maintaining operational efficiency. The findings emphasize that while network segmentation and micro-segmentation are not silver bullets, they represent indispensable layers of defense within a multi-faceted cybersecurity strategy. Organizations that successfully implement these strategies can significantly reduce the likelihood and impact of breaches, protect critical assets, and build resilience against future threats. This paper aims to provide a comprehensive guide for cybersecurity professionals, IT administrators, and policymakers to understand and adopt network segmentation and micro-segmentation. By integrating these strategies into their security frameworks, enterprises can fortify their defenses in the face of a constantly shifting threat landscape, safeguarding their infrastructure, data, and operations. (shrink)

: As organizations continue to embrace cloud computing, the migration of workloads, applications, and data from on-premises environments to the cloud remains one of the most critical tasks. However, migrating to the cloud is often seen as a daunting task due to the complexity, the risk of downtime, and the potential for service disruptions. Artificial Intelligence (AI) and automation have emerged as transformative technologies for cloud migration, providing solutions that enable a seamless, efficient, and (...) zero-downtime transition. This paper explores the role of AI in automating the cloud migration process, detailing how machine learning (ML) and AI-powered tools can optimize migration planning, reduce errors, and ensure continuous service availability. It highlights the key benefits of AI in workload assessment, data transfer, and post-migration optimization. Through the integration of advanced AI models, automation tools, and real-time analytics, organizations can achieve a smooth cloud migration process with minimal disruption to their business operations. The paper also examines real-world case studies where AI-powered cloud migration has been successfully implemented. (shrink)

Mainframe systems have a track record of reliability, but they need help in today’s fast-changing business world. These include high upkeep expenses and limited room for growth. This paper looks at three top cloud platforms—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—to update old mainframe programs. We compare them in detail, looking at what each does best. AWS has a wide range of tools to move data. Azure works well with cloud (...) and on-site systems and fits into company setups. GCP leads in AI and machine learning. Azure is the top option for updating mainframes, offering compatibility with various cloud configurations, integration with current company infrastructure, and sophisticated data analysis driven by AI. These conclusions provide valuable information for companies transitioning mainframe systems to the cloud, enabling expansion, reduced expenses, and future-ready IT systems. (shrink)

Cloud computing has revolutionized data management for businesses and individuals a like, ushering in an era of unprecedented accessibility and scalability. As demand for cloud services continues to surge, the imperative for efficient and secure systems becomes paramount. One approach to meeting this challenge is the consolidation of virtual machines onto fewer physical servers, optimizing resource utilization and yielding significant energy savings. Moreover, this consolidation strategy bolsters overall security by enabling more effective monitoring and control of virtual (...) machine instances. Through the implementation of encryption, access control mechanisms, and intrusion detection systems, sensitive data can be safeguarded against unauthorized access, thereby mitigating potential risks and vulnerabilities inherent in cloud computing environments. In the realm of cloud computing, security and efficiency are paramount concerns, driving innovation and evolution in system design and management. By consolidating virtual machines onto fewer physical servers, organizations can realize tangible benefits in terms of resource optimization and energy efficiency. Yet, beyond the operational advantages, this consolidation approach also enhances the security posture of cloud environments. Through the adoption of robust security measures such as encryption and intrusion detection, sensitive data can be shielded from unauthorized access, bolstering the integrity and trustworthiness of cloudbased systems. (shrink)

The growing reliance on cloud computing has introduced significant challenges surrounding data sovereignty, especially in a globalized digital economy. Data sovereignty refers to the legal and regulatory frameworks that govern where and how data can be stored, processed, and accessed based on the country or jurisdiction in which it resides. With cloud services enabling businesses to store and access data from multiple locations worldwide, navigating the complex web of national regulations has become a major (...) challenge. This paper explores the key issues of data sovereignty, the impact of differing legal frameworks on cloud services, and the challenges businesses face in ensuring compliance with international data protection laws. We also examine strategies for managing cross-border data flows, including data localization, compliance with General Data Protection Regulation (GDPR), and the role of cloud service providers in supporting data sovereignty. Through case studies and analysis of existing regulatory trends, this paper provides a comprehensive overview of the regulatory landscape and offers practical solutions for businesses to manage their data sovereignty obligations. (shrink)

The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas (...) industry are focused on first. Then,cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry. (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, which, while (...) enhancing efficiency, expand the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model (...) is proposed that is motivated by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. (shrink)

The rapid evolution of the financial sector, particularly in banking and fintech, necessitates continuous innovation in financial product development and testing. However, challenges such as data privacy, regulatory compliance, and the limited availability of diverse datasets often hinder the effective development and deployment of new products. This research investigates the transformative potential of AI-driven synthetic data generation as a solution for accelerating innovation in financial product development. Synthetic data, generated through advanced AI techniques such as Generative Adversarial (...) Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, can simulate real-world financial scenarios with a high degree of fidelity while preserving privacy and compliance standards. The use of synthetic data enables financial institutions and fintech companies to conduct rigorous testing, modeling, and validation of new products and services without relying on sensitive customer data. By generating realistic yet artificial datasets, organizations can explore a broader range of scenarios, including rare or extreme market conditions, thus enhancing the robustness and reliability of their financial models. This paper provides a comprehensive analysis of the underlying methodologies for synthetic data generation, focusing on their application to financial product development. It delves into the specific architectures and frameworks used in generating synthetic data, including GANs, VAEs, and synthetic minority over-sampling techniques (SMOTE), and examines their respective advantages and limitations. The paper also addresses the critical issue of ensuring the quality and utility of synthetic data, emphasizing metrics such as statistical similarity, privacy preservation, and applicability to real-world use cases. The discussion extends to the ethical and regulatory implications of deploying AI-driven synthetic data in finance, highlighting the need for transparent and explainable AI models to ensure trust and compliance. Moreover, the research explores practical case studies where financial institutions and fintech firms have successfully implemented synthetic data to develop and test new products, demonstrating significant reductions in time-to-market and development costs. One of the key contributions of this research is the exploration of how AI-driven synthetic data generation can facilitate the development of innovative financial products such as algorithmic trading strategies, risk management tools, credit scoring models, and fraud detection systems. By simulating diverse market behaviors and customer interactions, synthetic data enables the fine-tuning of algorithms and models to achieve higher accuracy and performance. Additionally, the paper discusses the integration of synthetic data generation into existing financial data ecosystems, proposing a framework for leveraging hybrid datasets that combine synthetic and real data to optimize model training and validation. The potential for synthetic data to drive collaborative innovation in finance is also considered, as it allows multiple stakeholders, including banks, fintech startups, and regulators, to share and analyze data without compromising confidentiality or privacy. The research also addresses the limitations and challenges associated with synthetic data generation in the financial domain, including issues related to data representativeness, overfitting, and the potential misuse of synthetic datasets. It emphasizes the need for ongoing research to develop more sophisticated algorithms that can generate highly realistic and diverse financial data. Furthermore, it identifies areas for future exploration, such as the use of federated learning and differential privacy techniques to enhance the security and privacy of synthetic data generation processes. The findings of this paper underscore the importance of AI-driven synthetic data generation as a catalyst for innovation in banking and fintech, providing a secure, scalable, and cost-effective means to develop, test, and validate new financial products and services. As the financial industry continues to evolve, the role of synthetic data in shaping the future of financial product development will become increasingly critical, paving the way for more efficient and innovative financial solutions. (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, which, while (...) enhancing efficiency, expand the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

Business intelligence (BI) and data visualization tools play a pivotal role in today's businesses by facilitating data-driven decision-making. Amazon's AWS, Microsoft's Azure, and Google's GCP are the three foremost cloud service providers, providing extensive solutions encompassing computing, storage, databases, and advanced cloud services. This paper delivers a thorough comparative analysis of three top BI platforms: Amazon Studio, Microsoft Power BI, and Google Looker. It evaluates their features, user-friendliness, integration options, pricing structures, and customer support. (...) By comparing specific features and examining real-world applications, this paper offers bits of knowledge that will assist organizations with picking the best tool to meet their particular requirements. The objective is to aid businesses and professionals in comprehending the strengths and weaknesses of each platform, thereby enabling them to make informed choices tailored to their unique needs. (shrink)

Abstract: The study aimed to determine the quality of services and its role in enhancing strategic flexibility, from the point of view of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data and consisted of (39) items, and the researchers used the comprehensive survey method, and the number of the study population was (536) individuals, where (434) questionnaires were retrieved, (...) and the recovery rate was (80.97%). The study revealed many results, the most important of which were: the existence of a high degree of approval by the study sample regarding the quality of services, as it was evident through the field of service quality obtaining a relative weight (79.90%), and the results of the study indicated that there is a moderate degree of approval by the members of the study sample. On the strategic flexibility, as it was evident by obtaining the area of strategic flexibility as a whole on a relative weight (60.44%). The results of the study revealed a statistically significant relationship between the quality of services and the strategic flexibility in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.490). The results of the study also revealed a statistically significant effect of the quality of services on the strategic flexibility in non-governmental hospitals in Gaza Strip. The study reached many recommendations, the most important of which were: Establishing an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital administration, to notify patients of dealing with the complaints they submit, and to work to provide all medical and health specialties in hospitals in place. The study, by making use of medical delegations visiting Gaza Strip, involving them in therapeutic operations, bringing in doctors and specialists from abroad, updating the criteria for measuring services provided to patients on an ongoing basis, based on patients' suggestions and complaints, developing facilities in hospitals, as well as updating medical devices and equipment Used in hospitals periodically and the need to work on appointing young people and people of energy, for vacancies in the hospitals under study, and the need to seek the help of an administrative staff that is distinguished by scientific and practical qualifications, and to work on updating information systems, archiving and networks through which data and information are transferred between departments, and finding mechanisms to be done from During which the information stored can be used to enhance the decision-making process. (shrink)

: As organizations continue to adopt cloud computing for its numerous advantages—such as scalability, cost-efficiency, and flexibility—the need to secure digital assets within the cloud has never been more critical. Cloud security faces unique challenges due to the distributed and multi-tenant nature of cloud services. Cyber-attacks, data breaches, and misconfigurations are among the prominent risks that threaten the integrity, confidentiality, and availability of cloud-based data and applications. In this paper, we explore CloudGuard, (...) an advanced security framework designed to protect organizations' digital assets in the cloud. This fortress security approach focuses on implementing multiple layers of protection through cutting-edge technologies, such as encryption, identity and access management (IAM), threat intelligence, and security automation. We also highlight the integration of AI and Machine Learning to improve threat detection and response times, and blockchain's potential in ensuring data integrity and privacy. Through a comprehensive analysis, this paper outlines practical strategies for organizations to adopt CloudGuard and strengthen their security posture against evolving cyber threats in the digital world. (shrink)

The increasing complexity and scale of modern computing needs have led to the development and adoption of cloud computing as a ubiquitous paradigm for data storage and processing. The hybrid cloud model, which combines both public and private cloud infrastructures, has been particularly appealing to organizations that require both the scalability offered by public clouds and the security features of private clouds. Various strategies for configuring and managing resources have been developed to optimize the hybrid (...) class='Hi'>cloud environment. These strategies aim to balance conflicting objectives such as cost-efficiency, performance optimization, security, and compliance with regulatory standards. This exploratory research focused on evaluating the efficiency and limitations of different configuration strategies in hybrid cloud environments. Findings indicate that each approach presents distinct advantages. Improving resource utilization and automating governance processes are significant advantages of Policy-based Resource Management, which leads to costeffectiveness. Intelligent routing of traffic is a feature of Cross-cloud Load Balancing, resulting in optimized performance and higher service availability. By centralizing control, the Hybrid Cloud Service Mesh allows for secure and streamlined cross-service communication. A notable feature of Cross-cloud Container Orchestration is its ability to simplify the migration of applications across diverse cloud environments. For immediate threat detection and regulatory compliance, real-time monitoring is facilitated by Log Management and Analytics. However, Policy-based Resource Management can be complex and inflexible. Extra costs for data transfer between different cloud providers are a drawback of Crosscloud Load Balancing. Additional network hops create latency issues in Hybrid Cloud Service Mesh configurations. If configured incorrectly, Cross-cloud Container Orchestration could expose the system to security risks. Finally, Log Management and Analytics require both ample storage and advanced analytical capabilities. (shrink)

ETL (Extract, Transform, Load) tools play a crucial role in contemporary data management by enabling the extraction, transformation, and loading of data from various sources into designated systems for analysis and reporting purposes. Major cloud service providers including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) give robust ETL solutions tailored to diverse business needs. This document presents a comparative assessment of the primary ETL tools offered by AWS, Azure, and GCP—namely, (...) AWS Glue, Amazon Data Pipeline, Azure Data Factory, Azure Synapse Studio, Google Cloud Dataflow, and Google Cloud Dataprep. The evaluation focuses on their respective features, integration capabilities, ease of use, and specific application scenarios, to aid organizations in selecting the most appropriate ETL tool based on their unique requirements and existing infrastructure. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (...) (LSTM) and Convolutional Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term (...) Memory (LSTM) and Convolutional Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi- cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated (...) a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The global shift to remote work has greatly increased the potential targets for cyberattacks, demanding stronger security measures than what Virtual Private Networks (VPNs) can provide. VPNs provide a bottom-line layer of security but don’t address the changing complexity of the distributed world of work. In this paper, we discuss advanced security frameworks and best practices to protect data and identity in a purely remote work setting, focusing on threat mitigation. The discussed key strategies include Zero Trust Architecture (ZTA), (...) Secure Access Service Edge (SASE), Endpoint Detection and Response (EDR), and multi-factor authentication (MFA). The paper also points out that employee cybersecurity training, risk assessments and integration with AI-driven threat intelligence systems. These approaches are demonstrated through case studies showing how organizations have navigated the shift to a remote-first setup. Using these cutting-edge strategies, enterprises can avoid the risk of remote work by continuing to be productive and secure. (shrink)

These days, Thin-client devices are continuously accessing the Internet to perform/receive diversity of services in the cloud. However these devices might either has lack in their capacity (e.g., processing, CPU, memory, storage, battery, resource allocation, etc) or in their network resources which is not sufficient to meet users satisfaction in using Thin-client services. Furthermore, transferring big size of Big Data over the network to centralized server might burden the network, cause poor quality of services, cause long respond delay, (...) and inefficient use of network resources. To solve this issue, Thinclient devices such as smart mobile device should be connected to edge computing which is a localized near to user location and more powerful to perform computing or network resources. In this paper, we introduce a new method that constructs its architecture on Thin-client -edge computing collaboration. Furthermore, present our new strategy for optimizing big data distribution in cloud computing. Moreover, we propose algorithm to allocate resources to meet Service Level Agreement (SLA) and Quality of Service (QoS) requirements. Our simulation result shows that our proposed approach can improve resource allocation efficiently and shows better performance than other existing methods. (shrink)

n the face of evolving cybersecurity threats, businesses must adopt more robust security frameworks to protect sensitive data. Traditional perimeter-based security models are no longer sufficient, especially with the rise of remote work, cloud computing, and mobile devices. Zero Trust Architecture (ZTA) is a modern security model that assumes no trust by default, whether inside or outside the corporate network. This paper explores the core principles of Zero Trust, how businesses can implement this model effectively, and the key (...) benefits it offers in protecting business data. Through the adoption of continuous authentication, least-privilege access, and micro-segmentation, businesses can significantly reduce their exposure to data breaches and insider threats. The paper also discusses the challenges businesses face when implementing ZTA, offering solutions to these issues. Lastly, we examine the future of Zero Trust and its potential to shape the future of cybersecurity. (shrink)

Cloud Computing provides vast storage facility. The requirement of this system is to improve the security and transmission performance in the cloud storage environment. Methods: This system provides two level of security for the cloud data. The Client Data Security Contrivance (CDSC) and Cloud Service Provider (CSP) Data Security Contrivance are the two methods which transforms the original data to cipher text. The security algorithm used in CDSC is Linguistic Steganography. Blowfish (...) algorithm is used in CSP Data Security Contrivance to convert the cipher text using a secret-key block cipher of 64-bits. This system is designed to increase security and to improve the transmission performance. Storage-cheating attack model and Privacy-cheating attack model are implemented to protect the data on cloud. Findings: This system increases security and improves transmission performance. The blowfish algorithm is secure against unauthorized attacks and runs faster than the popular existing algorithms. Storage-cheating attack model and Privacy-cheating attack model provides the data confidentiality for data stored in the cloud in higher way. Improvements: The system can be implemented in real cloud system in the future. (shrink)

Abstract: The study aimed to determine the strategic flexibility and its relationship to the level of quality of services provided, from the viewpoint of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data and consisted of (39) items, and the researchers used the comprehensive survey method, and the number of the study population was (536) individuals, where (434) questionnaires were (...) retrieved, and the recovery rate was (80.97%). The study revealed many results, the most important of which were: the existence of a moderate degree of approval by the study sample individuals on strategic flexibility, as it was evident through the area of strategic flexibility as a whole having a relative weight (60.44%). The study is on the quality of services, as it became clear through the field of service quality obtaining a relative weight (79.90%). The results of the study revealed a statistically significant relationship between strategic flexibility and the quality of services in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.490. The study reached many recommendations, the most important of which were: the need to work on appointing young people and those with potentials, because jobs are vacant in the hospitals under study, and the need to seek the help of an administrative staff with scientific and practical qualifications, and to work on updating information systems, archiving and networks through which data and information are transferred between departments And the creation of mechanisms by which stored information can be used to enhance the decision-making process, and an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital management, and to notify patients of dealing with the complaints they submit, and work to provide all Medical and health specialties in the hospitals under study, by making use of the medical delegations that visit Gaza Strip, involving them in the treatment processes, bringing in doctors and specialists from abroad, updating the standards related to measuring the services provided to patients on an ongoing basis, based on the suggestions and complaints of patients, and developing facilities in hospitals As well as updating the medical devices and equipment used in hospitals periodically. (shrink)

In Bulgaria, integrating technology and innovation is crucial for advancing sustainable healthcare disaster management, enhancing disaster response and recovery, and minimizing long-term environmental and social impacts. The purpose of the study is to assess the impact of modern technological innovations on the effectiveness of disaster management in health care in Bulgaria with a focus on Health Information Systems (HIS), Telemedicine, Telehealth, e-Health, Electronic Health Records, Artificial Intelligence (AI), Public Communication Platforms, and Data Security and Privacy. These (...) innovations, when integrated effectively, can significantly strengthen Bulgaria's preparedness and response capabilities, improving efficiency and reducing the environmental footprint of disaster interventions. Collaboration with technology experts, research institutions, and international organizations is essential for successful implementation. The research aims to assess how these technologies can enhance disaster management efficacy, adopting a methodical approach using Systematic Literature Reviews (SLRs). Systematic Literature Reviews (SLRs) utilize precise criteria for research selection and evaluation to reduce bias, systematically compiling and analyzing data from various studies to offer evidence-based insights. They are crucial for uncovering research gaps, indicating areas needing further exploration. Findings highlight the transformative potential of technology integration in streamlining communication, data management, and operational efficiency. The study underscores the urgent need for an integrated approach and a culture of innovation within the healthcare sector, emphasizing ongoing assessment and adaptation of technology-based strategies. While acknowledging limitations and calling for further research, the study provides valuable insights into Bulgaria's healthcare disaster management strategies and sets the stage for future investigations to refine and expand upon these strategies. It advocates for a strategic framework guiding the efficient integration of technology into disaster management, aiming for resilient and patient-centric healthcare systems. (shrink)

A Balanced Economic Model: The Feedback Loop Between Public and Private Sectors with MMT as a Stabilizing Mechanism -/- Introduction -/- Modern capitalism thrives on competition, profit motives, and consumer demand. However, the system is flawed because it allows extreme wealth inequality, market instability, and frequent economic crashes. Advertising, as an essential part of capitalism, manipulates consumer behavior to sustain profits. While this fuels economic growth, it also distorts the real needs of society. -/- A better alternative Is a hybrid (...) economic model where government-created jobs, social safety nets, and free public services coexist with a profit-driven private sector. The key to making this work is a feedback loop mechanism that keeps the two sectors balanced, ensuring economic stability while preventing extreme inequality and unnecessary market collapses. This essay explores how Modern Monetary Theory (MMT) can support this feedback loop economy to create a self-regulating system where both public and private sectors benefit from each other. -/- I. The Feedback Loop Between Private Companies and Government Economic Policies -/- A feedback loop mechanism is a self-regulating system where different components interact, adjust, and respond to maintain balance. In the economy, the public sector (government jobs, social safety nets, and public services) and the private sector (businesses, investments, and free markets) create a mutual dependency: -/- 1. Government Spending Creates Consumer Demand → Private Sector Growth -/- Government creates jobs with good salaries in infrastructure, education, healthcare, and technology. -/- This injects money into the economy, giving people the purchasing power to buy goods and services from private companies. -/- As demand increases, businesses grow, hire more workers, and expand operations. -/- 2. Private Sector Growth → Increases Government Revenue Without High Taxation -/- As private companies generate profits, they pay taxes and reinvest in expansion. -/- Strong consumer demand reduces the need for excessive taxation because economic growth itself funds public programs. -/- Businesses compete not only for profits but also for innovation and efficiency. -/- 3. Social Safety Nets Prevent Market Collapses -/- During recessions, government stabilizes the economy by expanding social programs (unemployment benefits, food assistance, healthcare). -/- This prevents demand from crashing, ensuring that businesses still have customers even in economic downturns. -/- As the economy recovers, government reduces spending, allowing the private sector to take the lead again. -/- This dynamic cycle ensures that economic booms and recessions are smoothed out, preventing extreme market crashes or mass unemployment. -/- II. The Role of MMT in Supporting the Feedback Loop -/- Modern Monetary Theory (MMT) provides the economic foundation for this model by proving that a sovereign government can spend money without being constrained by traditional budget rules. Unlike mainstream economic thinking, which suggests that governments must “balance the budget,” MMT argues that government spending should adjust based on economic conditions. -/- 1. Government-Backed Job Programs as an Economic Stabilizer -/- MMT allows governments to fund public jobs without relying solely on taxation. -/- If private companies cut jobs, the government can expand its workforce to absorb the unemployed. -/- When the economy stabilizes, people move back to higher-paying private jobs, reducing government spending. -/- 2. Preventing Inflation Through Resource Control -/- MMT acknowledges that inflation happens when there’s too much money chasing too few goods. -/- The government can remove excess money through targeted taxation and invest in productivity (e.g., infrastructure, education) to keep supply stable. -/- This prevents excessive inflation while keeping the economy growing. -/- 3. Continuous Money Circulation Without Debt Crises -/- Governments can print money to fund essential services, but they control inflation by investing in real production capacity (e.g., factories, renewable energy, technology). -/- This means public investment directly benefits private businesses, keeping both sectors in sync. -/- This MMT-backed feedback loop ensures that economic downturns do not lead to catastrophic business failures or mass unemployment. -/- III. The Role of Advertising in a Balanced Economic System -/- Advertising, while necessary for businesses, often manipulates consumer behavior in a way that creates artificial demand. In a feedback loop economy, advertising should be regulated to align with real consumer needs rather than exploiting psychological weaknesses. -/- 1. Ethical Advertising Policies -/- Companies should be incentivized to market based on quality and innovation rather than manipulation. -/- False advertising and monopolistic dominance should be regulated to prevent consumer exploitation. -/- 2. Consumer Education Programs -/- Public education should include critical thinking courses to help people make informed purchasing decisions. -/- This reduces the influence of manipulative advertising, ensuring that businesses compete fairly. -/- This ensures that the feedback loop remains healthy, preventing unnecessary overproduction and unsustainable consumer debt. -/- IV. Real-World Examples of a Balanced System -/- Some countries have successfully implemented elements of this feedback loop economic model: -/- Nordic Countries (Sweden, Norway, Denmark, Finland) -/- Have free healthcare, education, and social safety nets while maintaining a competitive private sector. -/- Use progressive taxation to prevent extreme inequality, ensuring that economic growth benefits everyone. -/- Germany -/- Uses government-backed vocational training programs to ensure a highly skilled workforce. -/- Strong public-private partnerships help both sectors support each other dynamically. -/- Japan -/- Government plays a major role in funding research, infrastructure, and innovation, allowing private businesses to thrive. -/- Uses state-owned enterprises strategically while ensuring a competitive private market. -/- These examples prove that a properly managed hybrid model is more resilient, fair, and stable than extreme capitalism or extreme socialism. -/- V. Conclusion: A Self-Regulating Economy for the Future -/- A feedback loop economic system provides the best of both worlds: ✔ Government ensures economic stability through public job creation and social safety nets. ✔ Private companies drive innovation and competitiveness with strong consumer demand. ✔ MMT allows government spending to adjust dynamically without unnecessary austerity measures. ✔ Advertising is reformed to encourage ethical business practices and informed consumer choices. ✔ Inflation is controlled through taxation, investment, and resource management. -/- By integrating these feedback loop mechanisms, we can create a self-regulating economic model where both public and private sectors reinforce each other, ensuring long-term stability, prosperity, and economic fairness. -/- Policy Framework for a Balanced Economic System: Global Best Practices -/- To fully integrate the feedback loop mechanism between the public and private sectors, specific policies must be implemented to ensure economic stability, innovation, and fairness. Below are real-world policy examples that demonstrate how different countries successfully manage their economies using a mix of government intervention and free market principles. -/- I. Public Job Creation as an Economic Stabilizer -/- One of the biggest issues in capitalism is economic recessions leading to mass layoffs, which then reduce consumer spending, creating a vicious cycle of economic decline. The solution is public sector job programs that absorb unemployed workers during downturns and allow them to transition back to private jobs when the economy improves. -/- Global Examples -/- ✔ United States (New Deal Programs – 1930s) -/- During the Great Depression, the U.S. government created millions of jobs in construction, public works, and conservation projects. -/- These projects boosted infrastructure while providing employment, helping to revive the economy. -/- ✔ Argentina’s Jefes de Hogar Program (2002-2006) -/- Aimed at mitigating mass unemployment during Argentina’s economic crisis. -/- The government hired unemployed citizens to work in community service, infrastructure, and public health projects. -/- Created social stability and demand for private goods, helping Argentina recover. -/- ✔ Germany’s Kurzarbeit Program (Ongoing) -/- In times of economic slowdown, the German government pays companies to keep workers employed at reduced hours rather than laying them off. -/- This prevents mass unemployment, ensuring that workers still have income and spending power. -/- Policy Recommendation: → Implement automatic public employment programs during recessions to keep demand stable and prevent economic crashes. -/- II. Social Safety Nets to Support Consumer Demand -/- A strong social safety net ensures that consumers have purchasing power even in economic downturns, keeping the private sector stable. -/- Global Examples -/- ✔ Nordic Model (Denmark, Sweden, Norway, Finland) -/- Provides free healthcare, education, and unemployment benefits, preventing extreme poverty. -/- Funded through progressive taxation, ensuring that the wealthy contribute fairly to society. -/- This creates a stable consumer base for private companies, benefiting the entire economy. -/- ✔ Universal Basic Income (UBI) Experiments (Finland, Canada, Kenya) -/- Finland tested a UBI program where unemployed citizens received a monthly payment regardless of employment status. -/- Results showed that recipients were more likely to start businesses and find better jobs, proving that social safety nets do not reduce work incentives. -/- ✔ Singapore’s Housing and Retirement System -/- The government helps citizens buy homes with public funds, ensuring stable housing. -/- A mandatory retirement savings system (Central Provident Fund) ensures that people have money after they retire. -/- These policies reduce financial anxiety, allowing people to focus on entrepreneurship and economic growth. -/- Policy Recommendation: → Implement universal healthcare, free education, and unemployment benefits to ensure that economic downturns do not collapse consumer spending. -/- III. Regulating Advertising to Align with Consumer Needs -/- Unregulated advertising manipulates consumer behavior, creating unnecessary demand and overconsumption, which destabilizes economies. Governments can regulate advertising to ensure ethical business practices. -/- Global Examples -/- ✔ European Union (Strict Advertising Laws) -/- The EU bans false advertising, ensuring that companies cannot make misleading claims about their products. -/- Certain junk food and alcohol ads are restricted to protect public health. -/- ✔ France’s Ban on Child-Targeted Advertising -/- Prevents unethical marketing towards children, reducing unnecessary consumerism. -/- ✔ Japan’s Culture of Product Quality Over Hype -/- Japanese companies emphasize product reliability and long-term value, reducing dependence on manipulative advertising. -/- Policy Recommendation: → Implement ethical advertising regulations to prevent false marketing, predatory ads targeting vulnerable consumers, and excessive consumer debt. -/- IV. Public-Private Partnerships for Innovation and Infrastructure -/- Rather than treating the government and private sector as separate entities, a well-balanced system interconnects them through public-private partnerships (PPPs) to drive innovation. -/- Global Examples -/- ✔ Germany’s Mittelstand Model (SME Support System) -/- The German government provides long-term financial support to small and medium-sized enterprises (SMEs), which are the backbone of the economy. -/- Ensures that private businesses have stable growth without excessive corporate monopolies. -/- ✔ China’s State-Led Economic Model (Strategic Government Investments) -/- The government invests heavily in infrastructure and technology, allowing private businesses to thrive with low-cost logistics and energy. -/- Balances capitalist competition with government support, ensuring stability. -/- ✔ United States (NASA & SpaceX Collaboration) -/- NASA funded SpaceX and other private space companies, leading to rapid innovation in space technology. -/- This proves that government funding can accelerate private sector breakthroughs. -/- Policy Recommendation: → Use government funding to support innovation and long-term infrastructure projects while allowing private businesses to benefit and expand. -/- V. MMT as a Tool for Economic Stability -/- Traditional economic models suggest that governments must “balance their budgets” like households, but this is a flawed concept. Modern Monetary Theory (MMT) argues that sovereign nations with their own currency (like the U.S., Japan, and the UK) can create money to fund essential services without relying on debt from private banks. -/- How MMT Works in Practice -/- ✔ Japan’s National Debt Strategy -/- Japan has one of the highest debt-to-GDP ratios in the world, yet it has no financial crisis because it borrows in its own currency. -/- Instead of austerity, Japan uses public investment to maintain economic stability. -/- ✔ U.S. Stimulus During COVID-19 -/- The U.S. government printed money to send direct payments to citizens, preventing a total economic collapse. -/- Inflation was controlled by adjusting taxation and investment in production capacity. -/- ✔ EU’s Post-COVID Recovery Fund -/- The European Union used public money to stabilize industries and workers, preventing extreme recessions. -/- Policy Recommendation: → Use MMT-based strategies to fund infrastructure, social programs, and innovation, ensuring economic stability without excessive reliance on private banking debt. -/- VI. Conclusion: Creating a Self-Regulating Economy -/- A feedback loop economy that integrates public job creation, private sector competition, social safety nets, and MMT-backed fiscal policies provides a self-regulating system that ensures: -/- ✔ Stable employment and consumer demand even during economic downturns. ✔ Innovation and competition in the private sector without excessive monopolies or market crashes. ✔ Government-funded infrastructure and social programs that benefit both workers and businesses. ✔ Ethical advertising that prevents consumer exploitation and unnecessary debt. ✔ Balanced inflation control through strategic taxation and investment. -/- By combining the best policies from different global models, we can build an economic system that minimizes business bankruptcies, prevents recessions, and maximizes innovation and economic fairness. -/- . (shrink)

Creativity and Intelligence: Emergent Properties of the Brain as a Balancing Mechanism for Overpopulation, Natural Disasters, and Diseases -/- The human brain is an extraordinary organ, capable of producing creativity and intelligence as emergent properties that allow humanity to address complex challenges. These traits are not merely tools for individual survival; they function as collective mechanisms to adapt to large-scale issues that threaten humanity’s balance with the environment. Overpopulation, natural disasters, and the prevalence of diseases and illnesses represent some of (...) the most significant pressures on human societies. Through the brain’s capacity for problem-solving and innovation, humanity has developed strategies to mitigate these challenges and maintain equilibrium within the universal law of balance. -/- The Brain as a Balancing Mechanism -/- At its essence, the brain operates as a dynamic system that processes external stimuli and generates adaptive responses. Creativity and intelligence arise from this system as tools to predict, analyze, and address challenges that disrupt balance. Overpopulation, natural disasters, and diseases disrupt human life on social, economic, and environmental levels, testing the brain’s ability to create solutions. -/- Creativity in Addressing Overpopulation -/- Overpopulation places immense strain on resources, ecosystems, and social structures, often leading to food shortages, environmental degradation, and urban overcrowding. The brain’s creative capacity has allowed humans to develop innovations to address these issues. Urban agriculture, such as vertical farming, is an example of how creativity has transformed food production in densely populated areas, reducing dependence on traditional farmland and conserving resources. Similarly, advancements in renewable energy technologies, such as solar and wind power, reduce the ecological footprint of growing populations, providing sustainable solutions to energy needs. -/- Creativity also plays a critical role in social policies and education. Family planning programs and reproductive health education have empowered individuals, particularly women, to make informed decisions, reducing population growth rates in many regions. These initiatives reflect the brain’s ability to create holistic solutions that align societal needs with the preservation of natural balance. -/- Intelligence in Managing Natural Disasters -/- Natural disasters, unpredictable and often catastrophic, require intelligent strategies to mitigate their impact. Intelligence enables humans to analyze data, predict disasters, and prepare for their consequences. Early warning systems for tsunamis, hurricanes, and earthquakes exemplify this, providing communities with critical time to evacuate and take protective measures. -/- Furthermore, intelligence drives the design of resilient infrastructure that can withstand disasters. For instance, earthquake-resistant buildings and flood mitigation systems demonstrate the brain’s capacity to transform scientific knowledge into practical solutions. These innovations save lives, reduce economic losses, and facilitate faster recovery after disasters, ensuring that human societies maintain their balance despite nature’s unpredictability. -/- Creativity and Intelligence in Combating Diseases and Illnesses -/- Diseases and illnesses have long been among humanity’s greatest challenges, especially in the context of overpopulation, which facilitates the rapid spread of infectious diseases. The brain’s emergent properties of creativity and intelligence have been critical in combating these threats through advancements in medicine, public health, and technology. -/- The development of vaccines, antibiotics, and antiviral drugs highlights humanity’s creative response to disease. For example, the eradication of smallpox and the near-elimination of polio are milestones achieved through creative and intelligent applications of medical science. During the COVID-19 pandemic, intelligence and creativity led to the rapid development of vaccines using mRNA technology, showcasing the brain’s ability to adapt to new challenges at an unprecedented pace. -/- Public health strategies, such as sanitation systems, clean water initiatives, and disease surveillance networks, also reflect the brain’s ability to create systemic solutions. These measures have significantly reduced the burden of communicable diseases, particularly in densely populated areas. Moreover, advancements in artificial intelligence and biotechnology continue to enhance humanity’s ability to detect, prevent, and treat illnesses. -/- The Interplay of Creativity, Intelligence, and Feedback Mechanisms -/- Creativity and intelligence act as feedback mechanisms that enable humans to respond to disruptions while maintaining balance within the universal law of nature. Overpopulation, natural disasters, and diseases disrupt this balance, prompting the brain to generate solutions that restore harmony. -/- For instance, urban planning that incorporates green spaces and sustainable infrastructure addresses the environmental strain caused by overpopulation. Similarly, the integration of traditional knowledge with modern technologies enhances disaster resilience. In the context of diseases, the collaboration of global institutions and local communities reflects the brain’s capacity to balance innovation with cultural and social considerations. -/- Challenges and Ethical Considerations -/- While creativity and intelligence empower humanity to address these challenges, they also carry significant ethical responsibilities. Technological advancements must align with principles of sustainability and equity. For example, while vaccines have saved millions of lives, unequal access to these innovations highlights the need for ethical distribution systems. Similarly, urban development projects must prioritize environmental conservation and community well-being to avoid exacerbating existing imbalances. -/- Additionally, the misuse of intelligence and creativity—such as the development of biological weapons or unsustainable industrial practices—can worsen the effects of overpopulation, natural disasters, and diseases. Ensuring that human ingenuity aligns with the principles of balance and harmony is essential to avoid unintended consequences. -/- Conclusion -/- Creativity and intelligence are emergent properties of the human brain that have enabled humanity to adapt to and mitigate the negative effects of overpopulation, natural disasters, and diseases. These traits function as balancing mechanisms, allowing humans to innovate and maintain harmony within the framework of natural laws. However, the application of these capabilities must be guided by ethical considerations and a commitment to sustainability. By aligning human creativity and intelligence with the universal law of balance, humanity can navigate its most pressing challenges and build a more resilient and equitable future. -/- . (shrink)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, (...) to analyze network traffic, detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and (...) machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The transition to a Circular Economy (CE) is a critical strategy in addressing global challenges related to resource depletion, waste generation, and environmental sustainability. Cloud computing, as a scalable and data-driven technology, has a significant role to play in enabling the circular economy by enhancing resource efficiency, waste reduction, and the optimization of product lifecycles. This paper explores how cloud computing facilitates circular economy practices by leveraging advanced technologies such as data analytics, Internet of Things (IoT), (...) and Artificial Intelligence (AI). We discuss how cloud platforms support businesses in tracking and managing resource flows, implementing closed-loop systems, and optimizing product design, repair, and reuse. By analyzing real-world case studies from industries such as manufacturing, retail, and energy, we highlight the potential of cloud-based systems in driving sustainable business models that minimize waste and maximize resource recovery. The paper also addresses challenges in the integration of cloud computing with circular economy practices and provides insights into the future potential of cloud technologies in advancing sustainability. (shrink)

The rapid growth of cloud computing has revolutionized data management, providing organizations with unmatched scalability and flexibility [1]. However, this digital transformation has also exposed critical vulnerabilities, especially in the face of increasingly sophisticated cyber threats [3]. As organizations migrate to cloud-based infrastructures, traditional security models are proving ineffective in safeguarding sensitive data. Zero Trust Architecture (ZTA) emerges as a proactive and robust security framework designed to address these challenges by enforcing strict access controls and verifying (...) every request for access, regardless of its origin [1][4]. This article explores the role of ZTA in mitigating emerging cybersecurity threats such as advanced persistent threats (APTs), insider attacks, and data breaches [5]. It examines how ZTA's principles—such as least privilege access, micro-segmentation, and continuous authentication—enhance data security in cloud environments [6]. Furthermore, the article discusses how ZTA integrates with encryption, identity and access management (IAM), and continuous monitoring to bolster organizational defenses against evolving cyber risks [2]. By aligning ZTA with regulatory compliance requirements, including HIPAA, GDPR, and CCPA, organizations can maintain both security and operational efficiency [7]. Through real-world case studies and best practices, this work provides a roadmap for implementing ZTA, offering actionable insights for organizations to mitigate risks, enhance cybersecurity posture, and safeguard their digital assets in an increasingly complex threat landscape [8][9]. (shrink)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, (...) to analyze network traffic, detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)