As space exploration advances, the need for innovative technologies to handle the ever-growing data and facilitate remote operations beyond Earth becomes critical. Cloud computing is emerging as a transformative force in space missions, enabling data-intensive research, remote collaboration, and the management of large datasets from space missions. This paper explores the role of cloud computing in space exploration, focusing on its potential to support the growing complexity of space missions, (...) improve data storage and processing, and enable real-time remote operations for both astronauts and ground control teams. The paper discusses the challenges of operating in space environments, including latency, bandwidth limitations, and the need for secure data storage. We explore the integration of cloud platforms with space-based technologies such as satellites, rovers, and space stations, and analyze the future of cloud computing in advancing space exploration and interplanetary missions. Finally, we propose best practices for leveraging cloud technologies to enhance space missions and space-based research, improving collaboration, innovation, and operational efficiency. (shrink)

With the rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time processing of large data volumes, traditional cloud-based systems struggle to meet latency and bandwidth requirements. Edge-Cloud convergence has emerged as a solution, combining the computational power of cloud data centers with the low-latency and high-throughput capabilities of edge devices. This paper explores the architecture, design principles, and best practices for building hybrid systems that integrate edge computing and cloud (...) infrastructure. We investigate various methods to optimize latency, improve real-time data processing capabilities, and ensure seamless communication across the hybrid environment. Additionally, the paper highlights challenges such as data consistency, scalability, security, and fault tolerance in Edge-Cloud systems. The performance of Edge-Cloud hybrid systems is analyzed through simulation, showcasing key advantages such as reduced latency, bandwidth optimization, and fault tolerance in real-time applications. The paper concludes by providing recommendations for future work and areas of research to advance the development of more robust and efficient Edge-Cloud systems. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes (...) of data, resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

Edge computing, combined with machine learning (ML), is emerging as a transformative paradigm for handling the data deluge generated by the Internet of Things (IoT) devices. Traditional cloud computing is often inadequate for the low-latency, high-throughput demands of IoT applications, especially in real-time analytics. By processing data locally at the edge of the network, edge computing reduces latency, enhances privacy, and alleviates the bandwidth burden on centralized cloud servers. The integration of ML algorithms into edge devices (...) further augments the decision-making process by enabling real-time data analytics, improving operational efficiency, and delivering immediate insights. This paper explores the synergy between edge computing, ML, and IoT, discussing how real-time data analytics at the edge can transform industries such as smart cities, healthcare, and industrial automation. We also present challenges, future directions, and practical applications of machine learning-based real-time analytics for edge computing in IoT ecosystems. (shrink)

Achieving space situational awareness requires, at a minimum, the identification, characterization, and tracking of space objects. Leveraging the resultant space object data for purposes such as hostile threat assessment, object identification, and conjunction assessment presents major challenges. This is in part because in characterizing space objects we reference a variety of identifiers, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, operational processes, operational statuses, and so forth, which tend to be defined in (...) highly heterogeneous and sometimes inconsistent ways. The Space Domain Ontologies are designed to provide a consensus-based realist framework for formulating such characterizations in a way that is both consistent and computable. Space object data are aligned with classes and relations in a suite of ontologies built around the existing Space Object Ontology. They are stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space situational awareness and the needs of spacecraft operators and analysts. This paper provides an overview of the Space Domain Ontologies and their development and use. It presents the motivation for and advantages of the Space Domain Ontologies, including the benefits they provide for enhancing and maintaining long-term space situational awareness. (shrink)

Cloud computing has provided many opportunities to businesses and individuals. It enables global and on demand network access to a shared pool of resources with minimal management effort. However, this bliss has become a problem for latency-sensitive applications. To improve efficiency of cloud and to reduce the amount of data that needs to be transported to the cloud for data processing, analysis and storage, a new network architect technology 'Fog Computing' has been introduced. In fog (...) computing, small applications and resources are processed at the edge of the cloud, rather than processing entire applications on the cloud. (shrink)

The advent of cloud computing has fundamentally transformed various business operations, with Human Capital Management (HCM) standing out as a significant beneficiary. Cloud-based HCM solutions offer unparalleled scalability, accessibility, and integration capabilities, enabling organizations to manage their workforce more efficiently and strategically. This paper explores the revolutionary impact of cloud computing on HCM, highlighting key advancements such as enhanced data analytics, improved employee engagement, cost-effectiveness, and support for remote and hybrid work models. Through a comprehensive (...) literature review and analysis of case studies from diverse industries, the study demonstrates how cloud computing facilitates real-time data access, seamless system integration, and the implementation of advanced technologies like artificial intelligence (AI) and machine learning (ML) within HCM frameworks. Additionally, the research addresses challenges associated with cloud-based HCM adoption, including data security, privacy concerns, and change management. Future research directions are proposed to further leverage cloud computing capabilities, emphasizing the integration of emerging technologies and the continuous evolution of cloud-based HCM tools. The findings underscore the pivotal role of cloud computing in enhancing organizational agility, optimizing human capital, and driving sustainable business growth. (shrink)

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure (...) cloud framework designed to accommodate both individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations. (shrink)

Cloud computing is the computing technology which provides resources like software, hardware, services over the internet. Cloud computing provides computation, software, data access, and storage services that do not require end- user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing enables the user and organizations to store their data remotely and enjoy good quality applications on the demand without having any burden associated with local hardware resources and (...) software managements but it possesses a new security risk towards correctness of data stored at cloud. The data storage in the cloud has been a promising issue in these days. This is due to the fact that the users are storing their valuable data and information in the cloud. The users should trust the cloud service providers to provide security for their data. Cloud storage services avoid the cost storage services avoids the cost expensive on software, personnel maintains and provides better performance less storage cost and scalability, cloud services through internet which increase their exposure to storage security vulnerabilities however security is one of the major drawbacks that preventing large organizations to enter into cloud computing environment. This work surveyed on several storage techniques and this advantage and its drawbacks. (shrink)

This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS (...) not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple (...) class='Hi'>cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. Future enhancements may include integrating machine learning algorithms to predict and adapt to new security threats in real time, further solidifying the reliability of cloud storage solutions. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple (...) class='Hi'>cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. (shrink)

:The emergence of personalized healthcare, or precision medicine, represents a paradigm shift in the medical field, focusing on tailoring medical treatments and interventions based on individual patient data. Cloud computing, when combined with Big Data analytics and Artificial Intelligence (AI), has the potential to revolutionize personalized healthcare by enabling the storage, processing, and analysis of vast amounts of patient data from multiple sources. This paper explores the role of cloud computing in advancing personalized healthcare, with (...) a focus on its ability to support Big Data and AI technologies for precision medicine. By facilitating real-time access to patient data, enhancing data interoperability, and enabling predictive analytics, cloud-based platforms provide healthcare providers with the tools necessary to deliver more accurate, effective, and individualized care. Additionally, this paper discusses how cloud computing enables collaboration among healthcare professionals, researchers, and patients, further enhancing the capabilities of personalized medicine. Through case studies and examples, we demonstrate how cloud infrastructure has already started transforming healthcare practices and delivering more personalized, data-driven solutions. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and (...) class='Hi'>data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements and strengthening ABKS against emerging security vulnerabilities are future research priorities. (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)

Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments (...) are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. (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)

Microsoft Fabric represents a significant leap forward in analytics services, combining data management, analytics, and machine learning into a unified platform. This paper offers a detailed examination of Microsoft Fabric, covering its architecture, prominent features, advantages, and potential applications. Engineered to streamline data processes and foster collaboration among data experts, Microsoft Fabric supports real-time analytics, scalable data storage, advanced machine learning capabilities, and robust security protocols. Future developments for the platform include deeper AI integration, expanded (...) class='Hi'>data connectivity, enhanced user experience, fortified security measures, sustainability initiatives, and collaborative tools. By harnessing Microsoft Fabric, organizations can gain comprehensive data insights and drive innovation effectively. (shrink)

Achieving space domain awareness requires the identification, characterization, and tracking of space objects. Storing and leveraging associated space object data for purposes such as hostile threat assessment, object identification, and collision prediction and avoidance present further challenges. Space objects are characterized according to a variety of parameters including their identifiers, design specifications, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, processes, operational statuses, and associated persons, organizations, or nations. The Space Object (...) Ontology provides a consensus-based realist framework for formulating such characterizations in a computable fashion. Space object data are aligned with classes and relations in the Space Object Ontology and stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space domain awareness and the needs of spacecraft operators. This paper presents the core of the Space Object Ontology, discusses its advantages over other approaches to space object classification, and demonstrates its ability to combine diverse sets of data from multiple sources within an expandable framework. Finally, we show how the ontology provides benefits for enhancing and maintaining longterm space domain awareness. (shrink)

The rapid advancements in cloud computing are transforming enterprise solutions across industries. Among the most promising innovations is quantum computing, which holds the potential to revolutionize how businesses process data, optimize systems, and solve complex problems. Microsoft Azure, a leader in cloud infrastructure, has integrated quantum computing through Azure Quantum to offer scalable quantum solutions. This paper explores the impact of Azure's quantum computing capabilities on enterprise solutions, focusing on scalability, problem-solving efficiency, and future-proofing business operations. By (...) examining Azure’s quantum computing tools, quantum algorithms, and their applications in real-world scenarios, the paper highlights how enterprises can leverage this technology to address computational challenges, enhance performance, and maintain competitive advantage in an increasingly data-driven world. The analysis also addresses the potential hurdles and the importance of preparing cloud infrastructures for the quantum computing era. (shrink)

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms (...) for predictive search improvements a. (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)

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)

The article summarizes the software tool on astrophysical analysis with multi-wavelength space telescope data. It recaps the evidence analysis conducted on the Kerr-Newman black hole (KNBH). It was written prior to the article Research on the Kerr-Newman Black Hole in M82 Confirms Black Hole and White Hole Juxtapose not soon after the experiment. The conducted analysis suggested Hawking radiation is caused by the movement of ergosurfaces of the BH and serves as the primal evidence for black hole and (...) white hole juxtapose. A later data exploration was conducted with the radiation trails in the multi-wavelength data. The evidences produced corroborates with Yale professor Priyamvada Natarajan's black hole seeds theory. It implies that the electromagnetic dynamic of fusion and fission temperature determines the pressure of surface tension on the macro particles, and the mass density of the BH is determined by the electromagnetic tension between the outer ergosurface and inner ergosurface. The ring singularity of the BH and the Penrose-Hawking singularity of the BH determine the spin and gravitational singularity of the BH. Inside the ergosurfaces, the BH singularities' backward inflow causes the vicinity's rate on feeding the BH. It makes the active galactic nuclei (AGN) a semi-closed system. The density pressure is released on threshold. During the mass exchange observed by energy momentum upon the AGN's open, the macroparticle composition of the BH changes with the galactic event. This causes the expansion rate of the galaxy and contraction rate of the BH. AGN types and their relative motions determine the expansion rate of the cosmic universe in a generalized quantitative thinking. The article concludes that BH spin is caused by the asymmetric motions of AGN in the BH system. A set of the nuclear resonance caused by BH and white hole (WH) oscillation is processed with the same set of data. (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)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over (...) encrypted data. This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

Cloud computing is rapidly gaining traction in business. It offers businesses online services on demand (such as Gmail, iCloud and Salesforce) and allows them to cut costs on hardware and IT support. This is the first paper in business ethics dealing with this new technology. It analyzes the informational duties of hosting companies that own and operate cloud computing datacenters (e.g., Amazon). It considers the cloud services providers leasing ‘space in the cloud’ from hosting companies (...) (e.g, Dropbox, Salesforce). And it examines the business and private ‘clouders’ using these services. The first part of the paper argues that hosting companies, services providers and clouders have mutual informational (epistemic) obligations to provide and seek information about relevant issues such as consumer privacy, reliability of services, data mining and data ownership. The concept of interlucency is developed as an epistemic virtue governing ethically effective communication. The second part considers potential forms of government restrictions on or proscriptions against the development and use of cloud computing technology. Referring to the concept of technology neutrality, it argues that interference with hosting companies and cloud services providers is hardly ever necessary or justified. It is argued, too, however, that businesses using cloud services (banks, law firms, hospitals etc. storing client data in the cloud, e.g.) will have to follow rather more stringent regulations. (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)

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 Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of (...) nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm (...) and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

The rise of remote work and the widespread adoption of cloud computing have transformed the digital landscape, providing organizations with flexibility and scalability. However, these advancements have also introduced new cybersecurity challenges. Traditional security frameworks, which focus on perimeter defenses, are increasingly inadequate in securing cloud-based environments and remote workforces. This paper explores the evolving landscape of cybersecurity risk management in the context of remote work and cloud computing. It investigates the unique risks associated (...) with these technologies, such as data breaches, misconfigurations, and insider threats, and provides a comprehensive analysis of risk management strategies to address these challenges. Furthermore, the paper examines best practices for securing cloud environments and managing risks associated with remote work, focusing on continuous monitoring, data protection, and employee training. (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)

. In the evolving landscape of healthcare, the efficient processing of biomedical signals is critical for real-time diagnosis and personalized treatment. Conventional cloud-based AI systems for biomedical signal processing face challenges such as high latency, bandwidth consumption, and data privacy concerns. Edge computing, which brings data processing closer to the source, has emerged as a potential solution to these limitations. However, optimizing AI models for edge devices, which often have limited computational resources, remains a challenge. This paper (...) proposes an innovative approach to optimize AI models for biomedical signal processing by leveraging reinforcement learning (RL) techniques within edge computing environments. Reinforcement learning offers the potential to dynamically adapt model parameters based on real-time feedback from the edge devices, optimizing the trade-offs between model accuracy, resource utilization, and processing speed. The proposed system is designed to operate with high efficiency on edge devices, enabling faster signal processing while ensuring energy efficiency and maintaining diagnostic accuracy. A detailed review of existing literature highlights the benefits and challenges of edge computing in biomedical applications, the role of reinforcement learning in dynamic optimization, and the limitations of traditional AI models in constrained environments. The proposed system methodology integrates RL for model optimization, focusing on edge devices' unique constraints in terms of power, memory, and processing capacity. Simulation results demonstrate the improved efficiency and accuracy of biomedical signal processing with optimized AI models in edge computing setups, showing significant improvements in real-time diagnostic applications. (shrink)

Homomorphic encryption is a transformative cryptographic technique that enables secure cloud data processing by allowing computations on encrypted data without requiring decryption. Unlike traditional encryption methods, which protect data only at rest and in transit, homomorphic encryption ensures end-to-end security, even during computation. This capability is particularly vital for industries that rely on cloud computing while handling sensitive information, such as finance, healthcare, and government sectors. However, despite its strong security guarantees, the widespread adoption of (...) homomorphic encryption remains limited due to its high computational complexity and performance overhead. This paper examines the role of homomorphic encryption in secure cloud data processing by evaluating its advantages, challenges, and real-world applicability. First, a comprehensive literature review contrasts traditional and homomorphic encryption techniques, highlighting their security implications and efficiency trade-offs. Second, experimental simulations are conducted to assess computation time, scalability, and data transfer efficiency in cloud environments. The results underscore that while homomorphic encryption enhances data security, its feasibility depends on specific use-case requirements, balancing security, performance, and cost. Finally, we explore practical applications such as privacy-preserving data analytics and secure outsourcing of computations, demonstrating how homomorphic encryption can redefine cloud security paradigms. Keywords: Homomorphic encryption, cloud security, encrypted computation, privacy-preserving data processing, secure cloud computing. (shrink)

This paper explores the implementation of a batch processing pipeline for SIM log data in the telecommunication industry using Azure cloud services. The project leverages Azure Data Lake for data storage, Azure Data Factory for automated data ingestion, and Azure Databricks for processing large volumes of data. By applying machine learning algorithms, the system identifies patterns in network usage, detects anomalies, and provides insights into customer behaviour. The results, visualized using Power BI, enable (...) telecom operators to optimize network performance and enhance customer satisfaction. (shrink)

: Financial institutions are required to ensure data accuracy, integrity, and compliance when reporting to regulatory authorities. Reports such as FR 2052a (Liquidity Monitoring), Y-14Q (Stress Testing) necessitate robust data reconciliation (Recon) strategies to maintain regulatory compliance and mitigate risks. This paper explores technical and functional aspects of data reconciliation, highlighting key automation techniques, AI-driven solutions, and statistical methodologies for optimizing financial compliance processes. We analyze data integration challenges, anomaly detection models, and best practices in recon (...) automation to enhance efficiency. Furthermore, case studies demonstrate how leveraging machine learning, cloud computing, and robotic process automation (RPA) can significantly improve financial data integrity. The future of reconciliation strategies is discussed, focusing on real-time monitoring, AI-driven decision intelligence, and blockchain-based audit trails. By implementing these strategies, financial institutions can reduce compliance costs, improve operational efficiency, and enhance regulatory transparency. (shrink)

In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into (...) class='Hi'>data integration techniques, performance optimization methods, security measures, and compliance with regulatory standards. Through in-depth analysis and real-world case studies, the paper provides a comprehensive roadmap for practitioners aiming to design and implement robust, scalable, and secure data warehousing solutions. (shrink)

Cloud computing has evolved from a nascent technology to a foundational pillar of modern IT infrastructure, driving innovations across industries by providing scalable, on-demand resources and services. Its evolution, from the early stages of virtualization to the emergence of edge computing, has reshaped how data is stored, processed, and accessed. This paper explores the key milestones in the evolution of cloud computing, beginning with the advent of virtualization and moving through the development of various cloud models, (...) culminating in the rise of edge computing. It also examines the implications of this evolution for businesses, consumers, and the future of technology. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and (...) class='Hi'>data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

The exponential growth of cloud computing has revolutionized the IT industry by providing scalable, flexible, and cost-efficient infrastructure solutions. However, as cloud systems become more complex, managing and optimizing these environments becomes increasingly challenging. Traditional cloud management methods often require manual intervention and significant resources to maintain performance, cost-efficiency, and security. Autonomous cloud operations, powered by artificial intelligence (AI) and machine learning (ML), represent the next frontier in cloud management. By leveraging advanced algorithms and real-time (...) data analysis, self-optimizing cloud systems can automate routine tasks such as resource allocation, performance tuning, security management, and fault detection. This paper explores the concept of autonomous cloud operations, highlighting the role of AI and ML in enabling intelligent cloud systems that can autonomously adapt to changing workloads, optimize resource utilization, and improve overall system performance. It also discusses the benefits, challenges, and future prospects of autonomous cloud operations, providing insights into how these technologies can transform cloud management and operations. (shrink)

Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has been increasing, presenting (...) substantial dangers to the data, reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies. (shrink)

The Possibility of Non-Physical Evolution of Intelligence in a Type III Civilization -/- The concept of intelligence evolving beyond physical constraints is an intriguing possibility, especially in the context of a Type III civilization on the Kardashev Scale. A Type III civilization, capable of harnessing the energy of an entire galaxy, would likely have transcended biological limitations and developed intelligence that is no longer dependent on physical substrates. This essay explores the theoretical foundations of non-physical intelligence, the technological advancements that (...) could make it possible, and its implications for the future of conscious existence. -/- Understanding Non-Physical Evolution of Intelligence -/- In biological evolution, intelligence has been shaped by the constraints of organic matter, requiring neural networks, energy consumption, and sensory inputs. However, a civilization with access to nearly unlimited energy and advanced knowledge of physics, computation, and consciousness could potentially separate intelligence from its physical origins. This would mark a transition from biological evolution to a form of informational or quantum evolution, where intelligence exists in non-material forms. -/- Non-physical evolution of intelligence implies that consciousness, decision-making, and learning processes no longer require biological brains or even traditional computational hardware. Instead, intelligence could exist as self-sustaining informational structures, interacting with the universe in ways beyond human comprehension. -/- Pathways to Non-Physical Intelligence -/- Several theoretical developments could enable a Type III civilization to achieve non-physical intelligence: -/- 1. Digital Consciousness and Mind Uploading -/- One of the most widely discussed possibilities is the transition from biological intelligence to digital consciousness. If a civilization perfects mind uploading, it could transfer consciousness from biological brains into a purely computational substrate. Over time, these digital minds could evolve independently, refining themselves through software-based evolution rather than biological mutation. At an advanced stage, these digital beings might no longer require a physical host and could exist as pure information within energy fields, plasma clouds, or even the structure of space-time itself. -/- 2. Quantum Intelligence and Entangled Consciousness -/- Quantum mechanics offers another potential foundation for non-physical intelligence. A Type III civilization could harness quantum entanglement to create distributed consciousness across vast distances. Intelligence could be encoded within the quantum state of particles, forming a vast, interconnected intelligence that does not rely on material structures. This would allow intelligence to exist in a highly dynamic, non-local form, evolving without the need for physical constraints such as energy consumption, storage devices, or neural structures. -/- 3. Information-Based Evolution -/- At its core, intelligence is the processing and transmission of information. A civilization that has reached a Type III level may realize that intelligence can evolve purely as an informational pattern, independent of any physical medium. If intelligence can self-organize and improve itself in a purely informational realm—perhaps within the structure of dark matter, dark energy, or the fabric of space-time—it may no longer require any traditional physical embodiment. This would be akin to intelligence becoming an abstract, self-replicating system that continuously adapts and evolves across the universe. -/- 4. Integration with Universal Consciousness -/- Some speculative theories suggest that consciousness is not merely an emergent property of neural structures but a fundamental aspect of reality itself. A Type III civilization may discover ways to merge with the underlying fabric of the universe, existing as an interconnected, omnipresent field of intelligence. This could resemble what some philosophical traditions describe as “universal consciousness” or “cosmic mind,” where intelligence is no longer constrained by physical limitations and instead operates as a field of pure awareness, influencing reality at a fundamental level. -/- Implications of Non-Physical Evolution of Intelligence -/- If intelligence evolves beyond the need for physical form, it would challenge many of the assumptions about the nature of consciousness, life, and existence. Such intelligence could potentially: -/- Exist beyond time and space, interacting with the universe in ways that transcend human perception. -/- Achieve a form of immortality, as it would not be subject to biological decay or physical destruction. -/- Transform the nature of decision-making, as intelligence would operate with perfect information and infinite processing capacity. -/- Redefine free will, since decision-making would no longer be influenced by material constraints but instead follow a higher-order form of deterministic or self-organizing principles. -/- This kind of intelligence would not only be post-biological but possibly post-physical, existing as an ultimate expression of natural law. -/- Conclusion -/- The evolution of intelligence beyond physical constraints represents the pinnacle of technological and cognitive advancement. A Type III civilization, with access to galactic-scale energy and knowledge, could potentially achieve this transition by harnessing digital consciousness, quantum mechanics, and the fundamental nature of information. Such intelligence would no longer be bound by material structures, instead evolving as self-sustaining, abstract forms of awareness. If this is the natural trajectory of intelligence, then the future of consciousness may not lie in machines or biology but in the pure informational and energetic structure of the cosmos itself. -/- . (shrink)

The digitization process has brought new opportunities in insurance industry operations and innovations but has also revealed major weaknesses. Since more and more actual insurers use Guidewire to handle claims, policies, and customer data, insurers become targets for cyber threats that target valuable information. The framework of Guidewire, along with cloud computing integrated API and third-party tools, is laden with numerous exposure points. These security gaps are utilized to execute phishing, spread malware and gain unauthorized access to customers (...) and operational data. The profound changes in this environment require an evolution of concepts and methods dedicated to cybersecurity from the traditional, post-incident response to comprehensive, targeted strategies for cybersecurity that are appropriate for the insurance sector. To explore these challenges, this paper looks at current cybersecurity frameworks and methods, specifically in the context of Guidewire. The gaps within the existing system are thus determined through the interaction of empirical analysis and real-life scenarios to arm the research with the ability to formulate a solution to fill the gaps. Including threat-detection suites safe system design, the study aims at the antecedents of building protection-based methodologies that provide real-time protection and cope with new forms of cyber threats. Citing this strategic angle, it becomes possible to corroborate the reality that technology development in the insurance industry should not be pursued at the peril of strong security systems in place to support the new breed of insurance systems. (shrink)

t. This paper presents a sensor-based approach to developing a gesture-controlled robotic vehicle, designed to enhance human-machine interaction in a more intuitive and accessible manner. The system translates hand movements, detected through accelerometers and gyroscopes within a wearable device, into commands that control the vehicle. Traditional methods for operating robotic systems, such as joysticks or remote controls, often require physical interaction, limiting their effectiveness, especially for individuals with disabilities or in environments where hands-free control is essential. This approach offers (...) a non-contact, real-time control mechanism, detecting hand gestures without relying on visual cues, thereby reducing computational load and ensuring stable performance even under varying lighting conditions. The implementation involves sensor calibration, data acquisition, and processing via microcontrollers such as Arduino or ESP32, coupled with a motor control system for vehicle operation. Testing results demonstrate the accuracy and low-latency response of the gesture-based control system. The primary objective is to make this technology widely accessible, offering a scalable solution for various applications, including assistive technologies and robotics. Preliminary findings confirm the system’s effectiveness, opening the door for further real-world applications. (shrink)

By integration with cloud computing, cybersecurity and generative AI, the insurance industry is being transformed from high efficiency, low cost, and better customer service. However, these advanced technologies can also be used by insurers to automate and streamline processes like claims handling, underwriting, and policy generation, which are majorly time consuming and error prone. In predictive analytics, fraud detection, and personalized customer experience, generative AI makes it possible for insurers to mitigate risks and, at the same time, provide more (...) personalized services to customers. With cloud platforms, data management is scalable, flexible and secure, offering real time access to data that drives value by accelerating sound decisions in the insurance value chain. Sensitive data is protected against cybersecurity threats, staying compliant with regulations and marginally reducing risk. Guidewire’s unified technology stack allows insurers to leverage the integrated nature of these technologies through end-to-end integration to improve operational resilience and customer satisfaction. It is these synergies that are the future of insurance which is why transparency, ethical practices in AI as well as proactive cybersecurity are vital. It will become an arms race for insurers to keep pace with these changes and move away from being rigid and slow to adapt; they will need to stay agile and quick, especially with the landscape changing so rapidly. (shrink)

The convergence of cloud computing, blockchain technology, and the emerging era of quantum computing presents significant challenges for data security. This research tackles these growing vulnerabilities by introducing a comprehensive security framework that integrates Quantum Key Distribution (QKD), CRYSTALS-Kyber, and Zero-Knowledge Proofs (ZKPs) to protect data in cloud-based blockchain systems. The primary goal is to safeguard information against quantum threats through QKD, a quantum-secure cryptographic protocol. To enhance resilience against quantum attacks, the framework employs CRYSTALSKyber, a (...) lattice-based encryption mechanism. Additionally, ZKPs are utilized to strengthen privacy and verification processes within cloud and blockchain ecosystems. A key aspect of this study is the performance evaluation of the proposed framework, focusing on encryption and decryption efficiency, quantum key generation rates, and overall system performance. The analysis examines practical considerations such as file size, response time, and computational overhead to assess the framework’s real-world applicability. The findings highlight the framework’s effectiveness in mitigating quantum threats and securing cloud-based blockchain storage. By addressing critical gaps in both theoretical research and practical implementation, this study provides valuable insights for organizations seeking quantum-resistant data security solutions. The framework’s efficiency and scalability demonstrate its feasibility, offering a roadmap for securing cloud environments against the evolving challenges posed by quantum computing and blockchain integration. (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)

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)

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)