This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data integrity, behavioral (...) analytics for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats. (shrink)

Cyberspace relies on information technologies to mediate relations between different people, across different communication networks and is reliant on the supporting technology. These interactions typically occur without physical proximity and those working depending on cybersystems must be able to trust the overall human–technical systems that support cyberspace. As such, detailed discussion of cybersecurity policy would be improved by including trust as a key value to help guide policy discussions. Moreover, effective cybersystems must have resilience designed into them. This paper (...) argues that trustworthy cybersystems are a key element to resilient systems, and thus are core to cybersecurity policy. The paper highlights the importance of trustworthiness for resilient cybersystems. The importance of trustworthiness is shown through a discussion of three events where trustworthiness was the target or casualty of cyberattacks: Stuxnet, hacking of communications and the Edward Snowden revelations. The impact of losing trust is highlighted, to underpin the argument that a resilient cybersystem ought to design in trustworthiness. The paper closes off by presenting a general set of policy implications arising from recognition of the interplay between trust, trustworthiness and resilience for effective cybersecurity. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

The ethical issues raised by cybersecurity practices and technologies are of critical importance. However, there is disagreement about what is the best ethical framework for understanding those issues. In this paper we seek to address this shortcoming through the introduction of a principlist ethical framework for cybersecurity that builds on existing work in adjacent fields of applied ethics, bioethics, and AI ethics. By redeploying the AI4People framework, we develop a domain-relevant specification of five ethical principles in cybersecurity: (...) beneficence, non-maleficence, autonomy, justice, and explicability. We then illustrate the advantages of this principlist framework by examining the ethical issues raised by four common cybersecurity contexts: penetration testing, distributed denial of service attacks (DDoS), ransomware, and system administration. These case analyses demonstrate the utility of this principlist framework as a basis for understanding cybersecurity ethics and for cultivating the ethical expertise and ethical sensitivity of cybersecurity professionals and other stakeholders. (shrink)

In 2024, the intersection of artificial intelligence (AI) and cybersecurity presents both unprecedented challenges and significant opportunities. This article explores the evolving landscape of AI-driven cyber threats, the advancements in AI-enabled security measures, and the strategic responses required to navigate these new realities. Leveraging statistics, trends, and expert insights, we delve into how organizations can enhance their cybersecurity posture in the face of sophisticated AI threats.

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)

Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the detection of (...) both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects to the internet, enabling improved functionality and convenience. However, the rapid adoption of IoT devices has raised significant cybersecurity concerns, as the vast number of connected devices presents new vulnerabilities that can be exploited by cybercriminals. These vulnerabilities are often the result of insecure device configurations, weak authentication mechanisms, and inadequate data protection. This paper explores the cybersecurity challenges associated with IoT, such as device security, network (...) security, and data privacy, and discusses the solutions that can help mitigate these risks. The paper further evaluates the role of standardization, encryption techniques, and intrusion detection systems in enhancing the security of IoT ecosystems. Finally, it proposes a framework for IoT security based on best practices and emerging technologies. (shrink)

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects to the internet, enabling improved functionality and convenience. However, the rapid adoption of IoT devices has raised significant cybersecurity concerns, as the vast number of connected devices presents new vulnerabilities that can be exploited by cybercriminals. These vulnerabilities are often the result of insecure device configurations, weak authentication mechanisms, and inadequate data protection. This paper explores the cybersecurity challenges associated with IoT, such as device security, network (...) security, and data privacy, and discusses the solutions that can help mitigate these risks. The paper further evaluates the role of standardization, encryption techniques, and intrusion detection systems in enhancing the security of IoT ecosystems. Finally, it proposes a framework for IoT security based on best practices and emerging technologies. (shrink)

The speed with which cyber threats are evolving is calling for fresh approaches to enhance cybersecurity and protection of privacy. Artificial Intelligence (AI) is proving to be a revolutionary factor in enhancing cybersecurity, providing powerful capabilities for intrusion detection, malware detection, and privacy protection. This article outlines an in-depth review of the use of AI in cybersecurity with particular reference to future directions and trends in research. Applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (...) format, we discuss more than 9,350 papers from the years 2004 to 2023. The research discerns 14 wide-ranging categories, such as AI- driven intrusion detection, privacy-preserving federated learning, malware classification via deep learning, and blockchain-assisted integration of cybersecurity. AI makes cybersecurity more effective despite the computationally extensive resource requirements, adversarial attack vulnerabilities, and ethical issues. This article prioritizes the use of further research in secure AI, normative AI-based practices, and regulation to further support privacy protection. Furthermore, new frontiers of quantum machine learning, AI explainability, and deepfake countermeasures are explored, potentially as routes for future security growth. (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)

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

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

Existing systems often lack a centralized platform for managing server and firewall operations, leading to fragmented oversight and delayed responses to cyber threats. This gap frequently results in increased vulnerabilities and operational inefficiencies within organizations. To address this issue, we propose the development of the Cybersecurity Portal, designed to provide real-time monitoring, predictive analytics, and streamlined resource allocation for server and firewall management. The portal will offer comprehensive insights into system performance, potential vulnerabilities, and proactive threat detection. By equipping (...) administrators with timely and relevant data, this platform aims to empower organizations to make informed security decisions and implement effective risk mitigation strategies. Ultimately, the Cybersecurity Portal seeks to enhance the overall cybersecurity posture of organizations, ensuring better protection of their digital assets against evolving threats. (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)

: 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 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 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 human factor in information systems is a large vulnerability when implementing cybersecurity, and many approaches, including technical and policy driven solutions, seek to mitigate this vulnerability. Decisions to apply technical or policy solutions must consider how an individual’s values and moral stance influence their responses to these implementations. Our research aims to evaluate how individuals prioritise different ethical principles when making cybersecurity sensitive decisions and how much perceived choice they have when doing so. Further, we sought to (...) use participants’ responses to cybersecurity scenarios to create profiles that describe their values and individual factors including personality. Participants (n = 193) in our study responded to five different ethically sensitive cybersecurity scenarios in random order, selecting their action in that scenario and rating and ranking of the ethical principles (i.e., Beneficence, Non-Maleficence, Justice, Autonomy, Explicability) behind that action. Using participants’ demographics, personality, values, and cyber hygiene practices, we created profiles using machine learning to predict participants’ choices and the principle of most importance to them across scenarios. Further, we found that, generalising, for our participants Autonomy was the most important ethical principle in our scenarios, followed by Justice. Our study also suggests that participants felt they had some agency in their decision making and they were able to weigh up different ethical principles. (shrink)

The rapid transition to remote learning during the early 2020 pandemic has catalyzed unprecedented changes in higher education cybersecurity infrastructure. This white paper presents a comprehensive framework addressing the multifaceted challenges of securing academic environments during this transformation. Our research examines not only traditional security concerns but also emerging challenges in student privacy, mental health considerations, geographical access disparities, and cross-institutional collaboration. Through empirical analysis of implementation across multiple institutions, we demonstrate how universities can adapt their security architectures to (...) support widespread remote access while maintaining data protection, academic integrity, and student wellbeing. Our findings show significant improvements across key metrics, including an 85% reduction in unauthorized access attempts, 99.9% availability of critical academic services, and a 40% reduction in student-reported technical anxiety during the transition period. This paper provides technical specifications, implementation guidelines, and empirical validation of our approach, offering a blueprint for institutions facing similar challenges in the rapidly evolving landscape of higher education. (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 research introduces to the design and implementation of the Cybersecurity Enabled Smart Controller for the grid connected microgrid which uses the combination of the Web technologies and the authentication mechanism which enables real time monitoring with help of the Dashboards built using Chart.js which is a framework of JavaScript. Due to the role and connection of the Microgrids with the critical infrastructure they are vulnerable to the cyber threats. This project leverages the use of HTML, CSS, JavaScript and (...) MongoDB to create a secure and responsive system ensuring the operational resilience and data integrity. By focusing on simplicity the project uses user friendly dashboard for monitoring the microgrid data , controlling the grid crafted with HTML, CSS and JavaScript allowing the users to visualize the performance metrics using Chart.js for real time data representation. This methodology focuses on creating secure communication channels which enables effective data management through MongoDB providing a responsive experience on the web application. This approach allows users to monitor grid operations continuously and the access management effectively. The findings focused on creating a user friendly interface using the HTML, CSS, JavaScript which give users the access to monitor and control microgrid operations seamlessly. Utilizing the Chart.js enabled the users to visualize real time performance metrics which enables the operators to make decisions quickly addressing operational efficiencies. This paper proposes a role-based access control system (RBAC) to protect and manage the smart controller of a grid-connected microgrid. The design integrates a login page developed with HTML, JavaScript, and MongoDB for authentication and user management, with three distinct roles: Admin, Power Operator, and User. Each role has access to specific functions in the dashboard, reflecting their respective permissions. The backend was implemented using Node.js and Express to monitor network traffic, detect potential threats, and prevent unauthorized operations. The results demonstrate that the system provides effective protection against cyber threats while ensuring that different users can access the functionalities appropriate to their roles. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps (...) and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

The increasing sophistication of cyber threats necessitates innovative and proactive cybersecurity measures. This paper explores the latest trends in cybersecurity, focusing on the role of Artificial Intelligence (AI), Zero Trust security, and blockchain technology. A review of the literature highlights significant advancements and persistent challenges, including the security of Internet of Things (IoT) ecosystems and human-centric vulnerabilities. Experiments were conducted to evaluate the efficacy of machine learning-based intrusion detection systems and Zero Trust implementation in a simulated environment. Results (...) demonstrate improved detection rates and reduced insider threat risks. This paper concludes by emphasizing future directions, such as quantum-resistant cryptography and collaborative cybersecurity frameworks, to secure the digital future. (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)

Where organizations used to rely on employees tenured with their company, the Great Resignation has presented new problems to organizational structure and fortification. Such a process usually leads to disrupted employee productivity and most importantly, increased vulnerability to cyber threats. Loyal workers, intentionally or unintentionally disloyal workers, and employees who leave the organization can compromise organizational confidential information, such as innovation, customer data, and other data that the organization considers to be highly valuable. Research has also revealed that workforce transition (...) time is also the highest-risk activity period for insiders, wherein activities like the unauthorized download of data or accidental data leaks when offboarding an employee are likely to occur. Moreover, there are difficulties, particularly for organizations that cannot apply adequate access controls and monitoring during the notice periods, making them much more sensitive to data leaks. Further, this paper examines these paramount cybersecurity threats and offers an organized framework for their mitigation. Explores how the risks can be reduced under this through policies like strong access controls, opaque data monitoring systems, and comprehensive offboarding. To get practical recommendations for managing the problem with data sharing, the example of using machine learning to realize the mechanisms for identifying anomalies and graph-theory-based mathematical models is given. Thus, this research provides a comprehensive set of procedures to address the consequences of the Great Resignation for organizations and safeguard their assets during worker turnovers. (shrink)

To evaluate the effectiveness of our approach to enhancing cloud computing network forensics by integrating deep learning techniques with cybersecurity policies. With the increasing complexity and volume of cyber threats targeting cloud environments, traditional forensic methods are becoming inadequate. Deep learning techniques offer promising solutions for analyzing vast amounts of network data and detecting anomalies indicative of security breaches. By integrating deep learning models with cybersecurity policies, organizations can achieve enhanced threat detection, rapid response times, and improved overall (...) security posture. This paper discusses the key steps involved in integrating deep learning models into network forensics, including data collection, model selection, real-time monitoring, and adaptive learning. Additionally, it highlights the importance of collaboration between cybersecurity experts and the cloud. Through case studies and experimental evaluations, we demonstrate the effectiveness and practicality of the proposed approach in enhancing cloud computing network forensics. Leveraging deep learning techniques offers promising solutions for detecting anomalies and identifying malicious activities within cloud networks. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve (...) response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

The rapid adoption of cloud computing has revolutionized the way organizations operate, offering unparalleled flexibility, scalability, and efficiency. However, it also introduces a new set of vulnerabilities and security challenges. This manuscript explores the integration of artificial intelligence (AI) in cybersecurity solutions to address these cloud vulnerabilities. By examining the current landscape, AI methodologies, and practical implementation strategies, we aim to provide a roadmap for enhancing cloud security through AI-powered solutions. -/- .

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

The increasing integration of digital technologies into every aspect of modern life has led to a growing concern over cybersecurity, particularly in the context of privacy. As organizations collect vast amounts of personal and sensitive data, the tension between securing this information and preserving individual privacy has become a critical ethical dilemma. This paper explores the ethical challenges faced by cybersecurity professionals, policymakers, and organizations when balancing the need for protection against the potential risks to personal privacy. It (...) discusses the implications of surveillance, data collection, and the ethical boundaries of cybersecurity practices. Additionally, it evaluates existing frameworks, legal regulations, and technological solutions that attempt to balance these concerns. The paper also proposes a set of ethical guidelines for cybersecurity practices, focusing on how organizations can protect data while respecting privacy rights. (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)

The application of Artificial Intelligence (AI) to health cybersecurity has led to a new era of opportunities and challenges. AI improves threat detection and mitigation but evokes concerns about data privacy, informed consent, algorithmic bias, and regulatory compliance. This article summarizes these vital issues by examining current challenges and suggesting ethical and legal frameworks to counter risks. Primary areas of emphasis are on lessening data, anonymizing data, minimizing bias, altering regulation, and facilitating international cooperation. The study needs to come (...) to know in-depth how AI can responsibly be used in healthcare cybersecurity without compromising patient rights. (shrink)

The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and microsegmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (...) (AI), machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape. (shrink)

COVID-19 is the new standard for forced changes in the workforce, forcing the adoption of remote and hybrid work environments globally. It was in the period of this rapid adoption of distributed work environments that new opportunities for flexibility and productivity were opened up, but fundamental weaknesses of conventional notions of cybersecurity were unveiled. As employees connect to company networks from a plethora of different places and different types of devices, situations were complex at the corporate level to ensure (...) that security measures remained adequately stringent. New threats came to the fore, such as endpoint devices that have added more risks since they were unsecured home networks and the use of personal devices (BYOD). All these changes raised the concern of a new security model for the enterprise whereby new, fresh methods of dealing with the issue emphasize strategies which accommodate the change, and we affirm the need to put in place security architecture that will help protect the valuable enterprise assets. This paper looks at the new challenges of defending the widely dispersed employee base and offers an integrated solution for protecting the modern enterprise. To address risks inherent in the current working environments, the framework seeks to address concerns of identity-centric security and zero-trust and apply emerging technologies in threat intelligence. Using knowledge acquired by current cyber assaults and actual programs, the research provides numerous substantial approaches to countering current and potential cyber threats. The research also identifies the main recommendations for enterprises operating in this environment, including the need for active management of risks and constant adaptation to threats. (shrink)

Abstract—The purpose of this study is to highlight and prove the positive impact in which blockchain could have on today’s IoT environment in terms of providing Cybersecurity for not just organizations, but other individuals who share data via the internet. The current IoT environs operates on a centralized cloud based server, meanwhile block chain operates on a decentralized server. The differentiation between the both plays a major role in the level of security they both provide; whereby, decentralized systems are (...) less vulnerable to cyber-attacks and centralized ones are not. In this report, real life illustrations are used to justify the knowledge that the block chain security could serve as a saving grace for internet users. Also, this form of security seems to be the only way to eradicate all forms of insecurity in supply chain and IoT breaches for businesses. An in depth understanding on how the block chain system operates would show why it’s held with such high hopes and how it can be beneficial for a wide range of industries. However, this research also highlights some difficulties that might arise in implementing the block chain system. It also gives ways in which the system can be gradually implemented. Firstly, the government needs to make it mandatory for businesses that deal with national based confidential data to implement block chain in their supply chain system to boost security. Secondly, top company officials like C.E. O’s need to take genuine interest in the block chain technology by investing in its development and making it a part of employee training, which in the long run would benefit the economy and business in return. Thirdly, a merge of more public and private businesses would help push the use of block chain further. Lastly, the government should make the block chain technology easily accessible, making the official permission to implement -easy and cost effective. (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)

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)

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

Cloud security remains a top concern for enterprises. This study introduces AI Security, a risk governance framework integrating AI, DevSecOps, and predictive analytics to enhance compliance automation and threat intelligence in cloud environments.

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and microsegmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (...) (AI), machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape. (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)

Artificial intelligence refers to the idea of programming computers to have human-like intelligence and the ability to imitate human behaviour. Machines show characteristics correlated with the human mind, such as learning and problem-solving. The current security systems are slow and insufficient. Artificial intelligence may aid in the improvement of these factors, as well as the detection rate of intrusion detection and prevention systems (IDPS).With the successful use of AI, the system would be more efficient and fast but this also brings (...) some risks and concerns. Therefore, it is equally important to manage both risks as well as concerns. Artificial intelligence improves the overall performance of security systems. Our research paper will help you know the various challenges, risks, and ways to resolve the issues and how new technologies can help fasten cybersecurity and make it more secure. It also informs about the current works going on in the world and what are different companies offering as help. (shrink)

The use of the internet in the current government environment continues to grow. The increasingly complex use of the internet can cause vulnerabilities to cyber attacks in information security, which include aspects of confidentiality, integrity, and service availability, so that it can disrupt the performance of public service delivery. The systematic literature review research method was carried out because of the large amount of information and data regarding cyber security strategies. This can be traced through various information in books, scientific (...) journals, newspapers, magazines, as well as sources of information from web pages/websites via the internet. This method is important in analyzing the concept of cyber security strategy in Indonesia. The obstacles that the government has are expired devices that are still used on local government networks, which may not have the latest updates, security devices such as antiviruses have expired, human resources have not fully supported this cybersecurity. Indonesia needs policies that regulate all elements related to cyber security. This infrastructure standard must comply with international standards for dealing with cyber warfare. (shrink)

This paper extensively develops and thoroughly creates a highly centralized and extremely efficient log collection system utilizing and leveraging the robust syslog server and comprehensively collects and gathers log data from the numerous hosts residing within the network, meticulously analyses and scrutinizes the detailed log files and promptly sends and transmits a critical alert message to the central host through secure and reliable SMS. This paper thoroughly and comprehensively addresses and tackles the increasingly growing and rapidly expanding need for extremely (...) robust and highly reliable cybersecurity measures and solutions in the vulnerable and sensitive power sector. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining (...) qualitative and quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

The heightened digitization of the healthcare industry has led to an exponential increase in sensitive patient data, which requires robust security models to prevent breaches, unauthorized access, and cyber attacks. Traditional security protocols are inadequate, and this has made it imperative to explore Artificial Intelligence (AI) and Blockchain as novel solutions. AI enhances healthcare cybersecurity by facilitating real-time anomaly detection, predictive analysis, and automated threat response, while blockchain offers decentralization, immutability, and secure data sharing. However, blockchain technology faces major (...) challenges for scalability and performance, as represented by lengthy transaction processing durations and high storage demands, elements that could deter its widespread adoption across the healthcare industry. To help counter these challenges, researchers are exploring Layer 2 scaling solutions, hybrid blockchain architectures, and off-chain storage strategies. In addition, cleanroom technology provides a controlled and secure environment for handling sensitive healthcare data, protecting privacy while also supporting AI-driven analytics and research collaboration. This article critically examines the intersection of blockchain and AI for healthcare security, in terms of challenges, use cases, and direction. Leveraging these technologies, health organizations can set up future-proof models of security that address data integrity, regulatory requirements, and resistance to future threats more effectively. (shrink)

Older adults’ growing use of the internet and related technologies, further accelerated by the COVID-19 pandemic, has prompted not only a critical examination of their behaviors and attitudes about online threats but also a greater understanding of the roles of specific characteristics within this population group. Based on survey data and using descriptive and inferential statistics, this empirical study delves into this matter. The behaviors and attitudes of a group of older adults aged 60 years and older (n=275) regarding different (...) dimensions of online safety and cybersecurity are investigated. The results show that older adults report a discernible degree of concern about the security of their personal information. Despite the varied precautions taken, most of them do not know where to report online threats. What is more, regarding key demographics, the study found some significant differences in terms of gender and age group, but not disability status. This implies that older adults do not seem to constitute a homogeneous group when it comes to attitudes and behaviors regarding safety and security online. The study concludes that support systems should include older adults in the development of protective measures and acknowledge their diversity. The implications of the results are discussed and some directions for future research are proposed. (shrink)

As digital transformation accelerates, organizations are increasingly reliant on cloud-based services for flexibility, scalability, and cost-efficiency. However, the proliferation of cloud technologies has introduced new, sophisticated cybersecurity challenges. The traditional perimeter-based security model is no longer sufficient in safeguarding critical data and applications. This paper explores the evolving landscape of cybersecurity in the context of cloud computing and presents strategies for mastering security in the digital age. We discuss the importance of securing cloud-native applications, the role of advanced (...) technologies such as artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing in securing cloud environments, and how organizations can implement a proactive security posture. By focusing on a multi-layered security framework, this paper provides insights into securing data, ensuring privacy, managing identity and access, and maintaining compliance in increasingly complex and dynamic cloud environments. (shrink)