The increasing complexity of modern networks has amplified the challenges associated with ensuring robust and scalable security. With the rapid evolution of cyber threats, traditional methods of network security management are often inadequate, leading to inefficiencies and vulnerabilities. Automation has emerged as a transformative approach to streamline network operations, enhance security postures, and reduce the margin of human error. This study explores the integration of Ansible, a powerful open-source automation tool, into network security workflows to deliver a comprehensive framework for (...) secure network automation. This research begins by examining the limitations of conventional network security management, emphasizing the time-intensive and error-prone nature of manual configurations. It highlights how automation, specifically using Ansible, addresses these issues by enabling consistent, repeatable, and scalable processes. Ansible’s ability to seamlessly integrate with diverse networking devices and platforms makes it an ideal choice for automating complex network environments. The methodology involves the development of automated playbooks for various security tasks, including firewall rule management, vulnerability scanning, configuration compliance checks, and incident response. By leveraging Ansible's declarative language, these playbooks are designed to be user-friendly and adaptable, ensuring ease of deployment across networks of varying sizes and complexities. The study also incorporates real-world use cases to demonstrate the tool's effectiveness in mitigating security risks while significantly reducing administrative overhead. Furthermore, the paper evaluates the impact of automation on key performance metrics such as response time to security incidents, accuracy of configurations, and overall system uptime. The findings indicate that integrating Ansible into network security workflows not only enhances operational efficiency but also fortifies security by minimizing misconfigurations and improving threat response times. These results underscore the importance of adopting automation as a critical component of modern network security strategies. In addition to technical insights, the study addresses potential challenges, such as initial implementation costs, skill gaps, and the need for continuous updates to automation scripts to align with evolving security requirements. It provides recommendations for organizations to overcome these obstacles, including investing in employee training and adopting iterative implementation strategies. The research concludes by affirming the transformative potential of automation tools like Ansible in revolutionizing network security management. By reducing reliance on manual processes, organizations can focus their efforts on proactive threat hunting and strategic planning, thereby fostering a more resilient security infrastructure. This paper serves as a comprehensive guide for network administrators and IT professionals seeking to leverage Ansible for secure network automation, providing both theoretical insights and practical solutions to enhance network security in an increasingly dynamic cyber landscape. (shrink)

: Ransomware remains one of the most significant cybersecurity threats, evolving rapidly with new attack vectors, encryption techniques, and extortion models. As we enter 2018, ransomware attacks have become more sophisticated, leveraging artificial intelligence (AI), automation, and emerging technologies to bypass traditional security measures. This paper analyzes the latest ransomware trends, including targeted attacks on critical infrastructure, Ransomware-as-a-Service (RaaS), and double/triple extortion tactics. Additionally, it explores advanced mitigation techniques such as AI-driven anomaly detection, zero-trust architectures, blockchain-based security solutions, and (...) proactive threat intelligence frameworks. By examining real-world case studies and industry best practices, this study provides insights into effective countermeasures and future directions for securing digital ecosystems against ransomware threats. The findings aim to assist cybersecurity professionals, policymakers, and organizations in strengthening their defense mechanisms against evolving ransomware threats in 2018 and beyond. (shrink)

The contribution of this paper is a comprehensive cybersecurity framework to secure cloud hosted Guidewire implementations by addressing critical security challenges such as threat detection, incident response, compliance, and system performance. Based on advanced technologies like machine learning, behavioral analytics and auto patching, the framework detects and mitigates known and unknown threats, incidentally zero-day exploit. The system does this through micro segmenting, behavioral anomaly detection, and automated patch orchestration in a way that does not render the system unperforming. (...) Key performance metrics of threat detection time (less than 3 seconds), incident response (patching within 5 minutes) and system availability (99.95% uptime) are all tested as the framework outperforms in all of these. Additionally, the framework ascertains industry standards such as GDPR and HIPAA via automated audit trails and ongoing monitoring. Compared to the currently deployed cloud security, the proposed approach, with a special focus on more secure the Guidewire application deployed in the cloud, offers a multi layered security approach to the modules specific to the Guidewire. Though powerful, the framework also suffers from complexity in its deployment, overhead in terms of performance and a reliance on cloud provider-specific features. The scalability will need to improve, and the product will need to support multiple clouds and integrate advanced technology such as quantum computing and AI-powered threat intelligence. This work sets a solid basis for the protection of cloud-based Guidewire systems against future cyber threats while maintaining operational continuity and system compliance. (shrink)

The rise in cyber threats targeting financial institutions has led to the adoption of artificial intelligence (AI) and deep learning models to mitigate risks and detect fraud in real-time. This paper explores AI-powered cybersecurity frameworks that enhance fraud detection, risk analytics, and compliance automation in financial transactions.

The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems to identify potential (...) cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

In an era of increasing cyber threats, ethical hacking has emerged as a pivotal practice in strengthening network security. Ethical hacking, also known as penetration testing, involves authorized attempts to breach a network or system to uncover vulnerabilities before malicious actors can exploit them. This research paper delves into the role of ethical hacking in assessing and mitigating network vulnerabilities to fortify defenses against cyberattacks. It emphasizes the strategic importance of ethical hacking in the context of evolving cybersecurity challenges and (...) explores its practical application in organizational security frameworks. The study begins by defining ethical hacking and its ethical and legal boundaries, distinguishing it from malicious hacking activities. It examines the methodologies and tools used by ethical hackers to identify vulnerabilities, simulate attacks, and provide actionable recommendations for remediation. By mimicking the tactics of cybercriminals, ethical hackers play a critical role in uncovering weaknesses in network configurations, software applications, and hardware systems. A core focus of the research is on the importance of integrating ethical hacking into a proactive cybersecurity strategy. The paper highlights how ethical hacking helps organizations stay ahead of potential threats by identifying vulnerabilities in real time and enabling swift mitigation. Case studies from various industries, such as healthcare, finance, and technology, are analyzed to demonstrate the tangible benefits of ethical hacking in preventing data breaches, minimizing downtime, and safeguarding sensitive information. The paper also explores the challenges associated with ethical hacking, including the need for skilled professionals, the potential for system disruptions during testing, and the importance of maintaining strict compliance with legal and regulatory standards. It discusses the ethical implications of penetration testing, particularly in balancing the need for security with respect for privacy and data protection. Advancements in tools and techniques used by ethical hackers are another significant aspect of this research. The paper investigates the use of artificial intelligence (AI) and machine learning (ML) to enhance vulnerability detection and streamline testing processes. It also addresses the rise of automated penetration testing tools and their role in increasing the efficiency and accuracy of ethical hacking efforts. The findings of this research underscore the necessity of ethical hacking as an essential component of modern cybersecurity frameworks. Organizations that adopt ethical hacking as part of their security strategy gain valuable insights into their vulnerabilities and can build resilient defenses against an everevolving threat landscape. By fostering a culture of continuous testing and improvement, ethical hacking contributes to the creation of a robust and secure network environment. This paper aims to serve as a comprehensive resource for cybersecurity professionals, IT managers, and policymakers, offering insights into the best practices, tools, and techniques of ethical hacking. By leveraging the principles of ethical hacking, organizations can effectively assess vulnerabilities, enhance their security posture, and protect critical assets in the face of mounting cyber threats. (shrink)

: The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems to identify (...) potential cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

The landscape of cybersecurity is constantly evolving, with adversaries becoming increasingly sophisticated and persistent. This manuscript explores the utilization of artificial intelligence (AI) to address these evolving threats, focusing on the journey from threat detection to autonomous response. By examining AI-driven detection methodologies, advanced threat analytics, and the implementation of autonomous response systems, this paper provides insights into how organizations can leverage AI to strengthen their cybersecurity posture against modern threats. Key words: Ransomware, Anomaly Detection, Advanced Persistent Threats (...) (APTs), Automated Threat Response and Artificial Intelligence. (shrink)

An increasing number of enterprises are using artificial intelligence (AI) to improve their cyber security and threat intelligence. AI is a type of AI that generates new data independently of preexisting data or expert knowledge. One emerging cyberthreat to systems that has been increasing is adversarial attacks. By generating fictitious accounts and transactions, adversarial attacks can interfere with and take advantage of decentralized apps that operate on the Ethereum network. Because fraudulent materials (such as accounts and transactions) used as (...) malicious payloads can be mistaken for legitimate data, detecting adversarial attacks can be difficult. This paper suggests a paradigm for cyber threat hunting in the Ethereum blockchain that makes use of Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN). By considering a variety of sources and data points, this technology enables decision support systems to automatically and rapidly identify threats posed by hackers or other harmful actors. The likelihood of a successful assault can be further decreased by using AI to find weaknesses in an organization's infrastructure. Because security operations centers (SOCs) need to quickly identify threats and take defensive action, this technology is particularly wellsuited for them. AI can give businesses an extra line of defense against increasingly complex threats by integrating intriguing and useful data items that would have otherwise gone unnoticed. (shrink)

An increasing number of enterprises are using artificial intelligence (AI) to improve their cyber security and threat intelligence. AI is a type of AI that generates new data independently of preexisting data or expert knowledge. One emerging cyberthreat to systems that has been increasing is adversarial attacks. By generating fictitious accounts and transactions, adversarial attacks can interfere with and take advantage of decentralized apps that operate on the Ethereum network. Because fraudulent materials (such as accounts and transactions) used as (...) malicious payloads can be mistaken for legitimate data, detecting adversarial attacks can be difficult. This paper suggests a paradigm for cyber threat hunting in the Ethereum blockchain that makes use of Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN). By considering a variety of sources and data points, this technology enables decision support systems to automatically and rapidly identify threats posed by hackers or other harmful actors. The likelihood of a successful assault can be further decreased by using AI to find weaknesses in an organization's infrastructure. Because security operations centers (SOCs) need to quickly identify threats and take defensive action, this technology is particularly wellsuited for them. AI can give businesses an extra line of defense against increasingly complex threats by integrating intriguing and useful data items that would have otherwise gone unnoticed . (shrink)

Abstract: As the frequency and sophistication of cyberattacks continue to rise, organizations face increasing challenges in safeguarding their digital infrastructures. Traditional cybersecurity measures often struggle to keep pace with rapidly evolving threats, creating a pressing need for more adaptive and proactive solutions. Artificial Intelligence (AI) has emerged as a transformative force in this domain, offering enhanced capabilities for detecting, analyzing, and preventing cyberattacks in real- time. This paper explores the pivotal role of AI in strengthening cybersecurity defenses by leveraging machine (...) learning algorithms, predictive analytics, and automation to anticipate and mitigate potential threats before they manifest. Furthermore, it examines AI's ability to evolve with emerging attack vectors, providing a dynamic response to an ever-changing threat landscape. The paper also addresses the limitations and ethical considerations surrounding AI-driven cybersecurity, advocating for a balanced approach to its deployment. Through this exploration, the research underscores how AI is redefining the future of cyber defense by shifting the focus from reactive to proactive strategies. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with Signature-Based Methods. (...) The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

This abstract summarizes, in essence, what generative AI means to the insurance industry. The kind of promise generated AI offers to insurance is huge: in risk assessment, customer experience, and operational efficiency. Natural disaster impact, financial market volatility, and cyber threat are augmented with techniques of real time scenario generation and modeling as well as predictive simulation based on synthetic data. One of the challenges that stand in the way of deploying these AI methods, however, is data privacy, model (...) reliability and interpretability. The insurance industry must address these issues if it is to comply with regulations like GDPR and CCPA or mitigate reputational risks, as anybody can make complaints about any company based on any perceived data breach. Indeed, generative AI models, often convoluted and biased, can propagate unfair outcomes, especially in pricing and policy access. Navigating these challenges requires a whole new frayed society of AI specialists in collaboration with the industry’s insurers to offer bespoke solutions that address the firm’s needs. Based on this work, future developments will center on designing and implementing ethical and regulatory frameworks around AI-driven decisions that are transparent, fair and sustainable. Generative AI has historically been the most feared phenomenon in the history of western civilization, but with predictive analytics and risk simulation capabilities taking shape, generative AI is set to disrupt the insurance industry and add real-time decision support, automating claims processing and personalized interaction. To successfully integrate generative AI, technical, ethical and regulatory challenges will need to be addressed, and a trust-driven ecosystem will emerge to serve evolving customer needs. (shrink)

The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to improve the accuracy (...) and effectiveness of detecting intrusions. The proposed framework integrates multiple layers of defense, including data preprocessing, feature extraction, classification, and post-processing, to provide a robust solution to IoT security challenges. This research explores the application of EML methods such as Random Forests, AdaBoost, and Gradient Boosting, combining them into a unified model to detect and mitigate various types of attacks in IoT environments. Experimental results demonstrate the superiority of the proposed framework in terms of detection accuracy, false positive rate, and computational efficiency. (shrink)

This paper examines the increasing importance of robust cybersecurity measures in the digital payments industry. As the volume and value of online financial transactions continue to grow exponentially, the sector faces a corresponding surge in cyber-attacks, necessitating advanced cybersecurity protocols. This study explores key cybersecurity measures and implementation strategies, including encryption, multi-factor authentication (MFA), tokenization, artificial intelligence (AI)- based fraud detection, and regulatory compliance, to safeguard digital payments against various cyber threats. Through a detailed review of existing literature, case studies, (...) and statistical analysis, the article provides strategic insights into how organizations can enhance security in digital payment ecosystems, maintain compliance, and achieve resilience in the face of evolving cyber threats. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with Signature-Based Methods. (...) The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (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 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)

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)

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 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 technology rapidly advances, the landscape of cybersecurity faces increasingly complex and evolving threats. This paper explores the future of cybersecurity, focusing on emerging threats and the corresponding mitigation strategies. Key threats include AI-driven attacks, ransomware evolution, quantum computing vulnerabilities, IoT security risks, cloud security challenges, and supply chain attacks. These threats have the potential to disrupt organizations and compromise sensitive data on an unprecedented scale. To address these challenges, the paper outlines effective mitigation strategies such as leveraging (...) AI and machine learning for defense, implementing Zero Trust architecture, adopting quantum-resistant cryptography, strengthening IoT security, improving cloud security practices, and managing third-party risks. A proactive, multi-layered approach to cybersecurity is essential to safeguard against the growing sophistication of cyber threats. This paper highlights the importance of continuous innovation and collaboration across industries to ensure a secure digital future. (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)

This project focuses on estimating reservoir inflows by integrating rainfall data, soil moisture levels in the catchment area, and releases from upstream reservoirs, coupled with an automated gate control system to prevent flooding in the basin. Utilizing hydrological models, the methodology predicts runoff from rainfall, adjusted for current soil moisture to enhance accuracy. Real-time data from upstream releases further refines inflow predictions. The automated system leverages predictive analytics and real-time monitoring to optimize gate operations, ensuring moderate water releases (...) to maintain safe reservoir levels and mitigate downstream flooding. This approach aims to enhance flood management and sustainable water resource use through advanced modelling and automation. (shrink)

The growing adoption of cloud computing has necessitated the development of AI-driven security frameworks to address threats in multi-cloud environments. This paper presents a cloud-native security model leveraging AI and zero-trust principles to enhance compliance, risk monitoring, and threat mitigation.

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models can identify (...) deviations from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

As automating technologies are increasingly integrated into workplaces, one concern is that many of the human workers who remain will be relegated to more dull and less positively impactful work. This paper considers two rival theories of meaningful work that might be used to evaluate particular implementations of automation. The first is achievementism, which says that work that culminates in achievements to workers’ credit is especially meaningful; the other is the practice view, which says that work that takes the form (...) of an open-ended practice is especially meaningful. Of the two, the practice view is the better tool for assessing the future of meaningful work, because achievementism is explanatorily inadequate in two ways. Moreover, the practice view can explain why the most meaningful forms of work cannot be automated. A procedure can only be automated as long as the steps are defined, whereas, in such work, which steps to take is indefinitely open to redefinition. This reveals the real threat to meaningful work to be a political-economic one. The relevant ethical questions there have to do with how much creative control workers retain in crafting their own jobs, when those jobs involve meaningful work in collaboration with machines; and how to liberate workers from jobs that do not involve meaningful work, which should be left to machines anyway. (shrink)

Is drastic action against global warming essential to avoid impoverishing our descendants? Or does it mean robbing the poor to give to the rich? We do not yet know. Yet most of us can agree on the importance of minimising expected deprivation. Because of the vast number of future generations, if there is any significant risk of catastrophe, this implies drastic and expensive carbon abatement unless we discount the future. I argue that we should not discount. Instead, the rich countries (...) should stump up the funds to support abatement both for themselves and the poor states of the world. Yet to ask the present generation to assume all the costs of drastic mitigation.is unfair.Worse still, it is politically unrealistic.We can square the circle by shifting part of the burden to our descendants. Even if we divert investment from other parts of the economy or increase public debt, future people should be richer, so long as we avert catastrophe. If so, it is fair for them to assume much of the cost of abatement.What we must not do is to expose them to the threat of disaster by not doing enough. (shrink)

Advances in AI are powering increasingly precise and widespread computational propaganda, posing serious threats to national security. The military and intelligence communities are starting to discuss ways to engage in this space, but the path forward is still unclear. These developments raise pressing ethical questions, about which existing ethics frameworks are silent. Understanding these challenges through the lens of “cognitive security,” we argue, offers a promising approach.

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 project aims to address the critical need for a robust explosion risk assessment tool in the oil and gas industry, particularly in oil and gas refinery environments where safety is paramount. Traditional methods of threat assessment are often manual, subjective, and limited in their capacity to provide real-time insights, which poses significant challenges for industry professionals in managing potential hazards. Our project leverages real-time weather data and machine learning algorithms to develop an advanced web application designed to identify (...) and assess threat zones within oil and gas refineries. By utilizing refinery-specific datasets, real-time weather parameters, and machine learning models, the application enables users to upload or access refinery data, select industry-specific details, and receive accurate risk predictions along with stability class assessments. The application integrates the OpenWeatherMap API to provide up-to-date weather information, while a Random Forest classifier used for gas classification and some other parameters are calculated and trained on relevant features such as wind speed, cloud cover, and insolation levels to predict explosion risks. Designed with an intuitive, user-friendly interface, this tool will be invaluable for safety engineers, refinery managers, and environmental professionals, facilitating proactive risk management and informed decision-making. By addressing limitations in current risk assessment methods—such as limited access to real-time environmental data, variable risk levels, and lack of predictive analytics—our project aims to provide a comprehensive, data-driven solution for managing safety and mitigating risks in high-stakes industrial environments. (shrink)

This book aims to provide a comprehensive analysis of Advanced Persistent Threats (APTs), including their characteristics, origins, methods, consequences, and defense strategies, with a focus on detecting these threats. It explores the concept of advanced persistent threats in the context of cyber security and cyber warfare. APTs represent one of the most insidious and challenging forms of cyber threats, characterized by their sophistication, persistence, and targeted nature. The paper examines the origins, characteristics and methods used by APT actors. It also (...) explores the complexities associated with APT detection, analyzing the evolving tactics used by threat actors and the corresponding advances in detection methodologies. It highlights the importance of a multi-faceted approach that integrates technological innovations with proactive defense strategies to effectively identify and mitigate APT. (shrink)

The rapid evolution of cyber threats has made traditional cybersecurity methods increasingly inadequate. Artificial Intelligence (AI) has emerged as a transformative technology in the field of cybersecurity, offering enhanced capabilities for detecting and responding to cyber threats in real time. This paper explores the role of AI in revolutionizing cybersecurity, focusing on its applications in threat detection, anomaly detection, and automated response systems. Through the use of machine learning algorithms, AI can analyze vast amounts of data, identify patterns, (...) and predict potential threats, making it an essential tool in modern cybersecurity strategies. This paper also highlights the challenges and ethical considerations associated with implementing AI in cybersecurity. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

This study investigates strategies for hotels situated near Taal Volcano to lessen the impact of natural disasters, specifically volcanic eruptions. The hospitality industry in this region faces a unique threat due to the volcano's activity. This research addressed a critical gap by implementing effective preparedness and response plans. A qualitative multiple case study design was employed by the researchers. Seven (7) hotel executives from different hotels situated in Batangas who had directly encountered and navigated the impacts of natural disasters (...) were selected through the purposive sampling technique. The findings revealed several common natural hazards faced by hotels include Taal volcano eruptions, typhoons, earthquakes, ashfall, and sulfur dioxide emissions. Despite of this natural hazards, hotel executives were still capable of safeguarding their guests by implementing strategies including evacuation procedures, staff training, communication and coordination and preventive measures. In conclusion, to overcome the challenges identified in disaster preparedness, hotels in Batangas should consider several strategic actions. Hotels are recommended to take several crucial actions to enhance their preparedness and response capabilities to natural hazards. Adhering to safety regulations will help hotels maintain a high level of preparedness and minimize risks associated with natural hazards, ultimately safeguarding the well-being of guests and staff alike. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently (...) interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (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 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)

The global Climate Change has unprecedented consequences in terms of scale and severity over human life. The accumulation of greenhouse gases and CFCs has increased environmental deterioration which is called global warming. Erratic changes in weather, brutal blizzards and floods, vicious heat wave etc. are only some of the effects of climate change. But the most dangerous effect of climate change is the melting of ice caps on the poles due to which sea levels are rising dangerously and life at (...) the poles is threatened. It is also a reality that habitation in several countries , not very much above the sea-level, for example, Maldives, Sri Lanka, Bangladesh and Indonesia, which have a threat of huge displacement of human beings and domestic animals due to global warming. The survival of millions of people in developing countries like India is more vulnerable to the negative impacts of climate change because of their limited capacity in terms of human financial and institutional resources. India is the world’s fourth largest economy and fifth largest greenhouse gas (GHG) emitter, accounting for about 5% of global emissions. India’s emissions increased 65% between 1990 and 2005 and are projected to grow another 70% by 2020. The major impacts of Climate Change on India are major shifts in temperature, effect on monsoons, rising of sea levels, change in crop cycle, etc. India has prepared the National Action Plan on Climate Change (NAPCC) in 2008 for energy efficiency and sustainable development. India is a part to the United Nations Framework Convention on Climate Change. Under the Ministry of Environment and Forests, a National Inventory Management System (NIMS) has been formed to generate a comprehensive knowledge base on scientific issues related to climate change and mitigation. This paper highlights the issue of impacts of climate change and measures adopted by the government to minimize the dangers. (shrink)

The application layer of the Open Systems Interconnection (OSI) model plays a critical role in enabling end-user interaction with network services, making it a primary target for cybersecurity threats. This paper reviews contemporary research on securing the application layer, addressing emerging threats such as injection attacks, malware, phishing, and zero-day vulnerabilities. Key strategies, including secure development practices, application firewalls, cryptographic protocols, and user education, are explored to mitigate these risks. The challenges of an evolving threat landscape and resource constraints (...) are discussed, along with future directions leveraging artificial intelligence, blockchain, and zero trust architecture to enhance security. The findings underscore the importance of a multifaceted approach to safeguarding the application layer in an increasingly hostile cyber environment. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

ABSTRACT: So far in this book, we have examined algorithmic decision systems from three autonomy-based perspectives: in terms of what we owe autonomous agents (chapters 3 and 4), in terms of the conditions required for people to act autonomously (chapters 5 and 6), and in terms of the responsibilities of agents (chapter 7). -/- In this chapter we turn to the ways in which autonomy underwrites democratic governance. Political authority, which is to say the ability of a government to exercise (...) power, may be justifiable or not. Whether it is justified and how it can come to be justified is a question of political legitimacy. Political legitimacy is another way in which autonomy and responsibility are linked. This relationship is the basis of the current chapter, and it is important in understanding the moral salience of algorithmic systems. We will draw the connection as follows. We begin, in section 8.1, by describing two uses of technology: crime predicting technology used to drive policing practices and social media technology used to influence elections (including by Cambridge Analytica and by the Internet Research Agency). In section 8.2 we consider several views of legitimacy and argue for a hybrid version of normative legitimacy based on one recently offered by Fabienne Peter. In section 8.3 we will explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems—among them PredPol and the Cambridge Analytica-Facebook-Internet Research Agency amalgam—can hinder that legitimation process and conflict with democratic legitimacy, as we argue in section 8.4. We will conclude by returning to several cases that serve as through-lines to the book: Loomis, Wagner, and Houston Schools. -/- The link below is to an open-access copy of the chapter. (shrink)

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models can identify (...) deviations from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

There is not as much resistance to COVID-19 mitigation as there seems, but there are structural features that make resistance seem worse than it is. Here we describe two ways that the problem seeming to be worse than it is can make it worse. First, visible hesitation to implement COVID-19 responses signals to the wider society that mitigation measures may not succeed, which undermines people’s conditional willingness to join in on those efforts. Second, our evaluations of others’ willingness (...) to implement these measures are informed by our attempts to mind-read them. Yet attempts to mind-read groups often mislead us, because groups invariably act from diverse motives whereas mind-reading works best when identifying relatively stable and consistent motivations. This means that a small minority of people refusing to implement measures can have an outsized prominence that prompts mind-reading to diagnose widespread hesitation. These two factors form a feedback loop with each other: we see some people’s hesitation, which prompts us to mind-read other people as being more uncertain about the responses than they actually are, which undermines our confidence in the responses, which in turn encourages others to mind-read this hesitation, which further undermines confidence. (shrink)

This research paper investigates the transformative applications of data visualization techniques in the realm of financial crime detection, with a specific emphasis on addressing the challenges posed by credit card fraud and money laundering. The abstract explores the intricate landscape of visualizing financial data to uncover patterns, anomalies, and potential illicit activities. Through a comprehensive review of existing methodologies and case studies, the paper illuminates the pivotal role data visualization plays in enhancing the efficiency and accuracy of fraud detection systems. (...) By synergizing advanced visualization tools with machine learning algorithms, the study aims to provide insights into how financial institutions can bolster their defenses against evolving threats. Ethical considerations, usability, and the real-world impact of data visualization in combating financial crime are also scrutinized. This research contributes to the evolving discourse on leveraging visualization technologies to fortify financial systems against illicit activities, fostering a proactive and responsive approach to safeguarding economic ecosystems. (shrink)

Suppose we are about to enter an era of increasing technological unemployment. What implications does this have for society? Two distinct ethical/social issues would seem to arise. The first is one of distributive justice: how will the efficiency gains from automated labour be distributed through society? The second is one of personal fulfillment and meaning: if people no longer have to work, what will they do with their lives? In this article, I set aside the first issue and focus (...) on the second. In doing so, I make three arguments. First, I argue that there are good reasons to embrace non-work and that these reasons become more compelling in an era of technological unemployment. Second, I argue that the technological advances that make widespread technological unemployment possible could still threaten or undermine human flourishing and meaning, especially if they do not remain confined to the economic sphere. And third, I argue that this threat could be contained if we adopt an integrative approach to our relationship with technology. In advancing these arguments, I draw on three distinct literatures: the literature on technological unemployment and workplace automation; the antiwork critique—which I argue gives reasons to embrace technological unemployment; and the philosophical debate about the conditions for meaning in life—which I argue gives reasons for concern. (shrink)

In intelligence, the ontological problem is related to the nature and characteristics of entities that threaten and are threatened. Developing a threat ontology requires a taxonomy. An ontology for threat analysis and action must be able to shape ontological distinctions between potential and viable threats. This provides a better understanding of how threats (ie intentions, capabilities, and opportunities) can exist and can be changed over time. Escalating threats from a state of potency to a state of viability could (...) be avoided by using appropriate threat mitigation techniques. DOI: 10.13140/RG.2.2.25163.54569 . (shrink)

Anamoly detection in videos plays an important role in various real-life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Nowadays, there has been a rise in the amount of disruptive and offensive activities that have been happening. Due to this, security has been given principal significance. Public places like shopping centers, avenues, banks, etc. are increasingly being equipped with CCTVs to guarantee the security of individuals. Subsequently, this inconvenience is making (...) a need to computerize this system with high accuracy. Since constant observation of these surveillance cameras by humans is a near-impossible task. It requires workforces and their constant attention to judge if the captured activities are anomalous or suspicious. Hence, this drawback is creating a need to automate this process with high accuracy. Moreover, there is a need to display which frame and which parts of the recording contain the uncommon activity which helps the quicker judgment of that unordinary action being unusual or suspicious. Therefore, to reduce the wastage of time and labour, we are utilizing deep learning algorithms for Automating Threat Recognition System. Its goal is to automatically identify signs of aggression and violence in real-time, which filters out irregularities from normal patterns. We intend to utilize different Deep Learning models (CNN and RNN) to identify and classify levels of high movement in the frame. From there, we can raise a detection alert for the situation of a threat, indicating the suspicious activities at an instance of time and spray the smoke spray. (shrink)

Conservation can occur anywhere regardless of scale, political jurisdiction, or landownership. We present a framework to help managers at protected areas practice conservation at the scale of relationships. We focus on relationships between stakeholders and protected areas and between managers and other stakeholders. We provide a synthesis of key natural resources literature and present a case example to support our premise and recommendations. The purpose is 4-fold: 1) discuss challenges and threats to conservation and protected areas; 2) outline a relationship-scale (...) approach to address conservation threats; 3) describe the tools and techniques that can be used to implement this approach; and 4) present a case example from rural Alaska, USA, to illustrate relationship-scale conservation. Our case example illustrates how aspects of this approach to conservation were applied to address a wildlife population decline. Tools needed to implement relationship-scale conservation include 1) collecting and documenting narratives of place; 2) measuring and monitoring trust and commitment; and 3) identifying and mitigating threats. We recommend that planners and managers, working with their research partners, redefine and refocus their goals and objectives to include these practices. Doing so will enable them to gain substantial applied knowledge about their stakeholders and foster and maintain place relationships as desired outcomes of conservation. The ultimate outcome is a better prognosis for long-term global survival of protected areas and biodiversity. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. (shrink)