Threat Landscape Research Articles

“The Effect of Cyber Risk on Banks Profitability in Egypt”: An Empirical Analysis

Recent years have seen an impressive increase in the integration of financial technology or fintech in the global banking industry, resulting in product, service, and operational developments. Nowhere has this shift in focus been more pronounced than in fintech, a sector that has seen remarkable growth in Egypt. Nevertheless, this is not without its difficulties since integrating technology means that their banks are more susceptible to cyber risk, which could decrease its profitability. This research aims to assess whether cyber risk exerts a statistically significant negative impact on the profitability of banks in Egypt, utilizing a sample of 16 banks spanning the years 2017 to 2022. The study employs panel data analysis through STATA 14 for its investigation. The findings demonstrated that cyber risk has a negative significant effect on bank profitability, and this assumption was supported by the study's findings. According to the findings, cyber risk has a significant and negative impact on both ROA and GPM. As a consequence of this, it is necessary to incorporate preventative measures in order to deal with the constantly shifting cyber threat landscape. It is also essential to highlight the significant role that technology and data security play in ensuring that the banking industry remains robust and profitable in the digital era.

Dynamic Multi-Method Allocation for Intent-based Security Orchestration

In today's dynamic cybersecurity landscape, static and deterministic services orchestration which does not consider security as part of the orchestration process are proving insufficient against the evolving threat landscape. Security must be an intrinsic part of the orchestration processes. In this regard, this paper introduces an innovative paradigm shift: Intent & AI-based Optimized Security Orchestration. On the one hand, leveraging the capabilities of an Intent-based solution, this approach enables proactive and reactive threat mitigation in next generation heterogenous environments of the computing continuum, abstracting and homogenizing the complexity of underlying technologies. On the other hand, leveraging the capabilities of a dynamic allocation approach that applies different techniques for selecting the most suitable enforcement point (hardware/software) as well as the most suitable allocation for deploying/configuring them, always considering security properties during decision stages. Thus, the solution allows organizations adaptively optimizing resource allocation considering intent-based security requirements. The implementation considers different algorithms to perform the allocation decision depending on a variety of parameters. The performance has been also provided for validating the proposed solution. The results show that combining the security orchestrator with a Dynamic Allocation Engine improves the efficiency of decision making due to the ability to dynamically choose which algorithm is the most appropriate to solve the assignment problem in the best possible way and in the shortest possible time.

State Cyber Warfare: The Strategic Shift Towards Private Sector Targets

The strategic targeting of private sector infrastructure by state-sponsored cyber actors represents a fundamental transformation in modern warfare, with profound implications for national security and economic stability. Drawing on a comprehensive literature review and theoretical analysis, this research examines the drivers and consequences of this evolving cyber threat landscape. The findings indicate this strategic shift toward private sector targets serves multiple objectives for state actors, including technological competition, economic disruption, and the exploitation of vulnerabilities in critical infrastructure. By increasingly focusing cyber attacks on private enterprises rather than traditional government or military targets, nation-states are able to achieve strategic aims while maintaining plausible deniability and minimizing the risk of direct military confrontation. The study proposes new frameworks for enhanced public-private cooperation in cyber defense, as well as targeted policy measures to bolster the protection of essential private sector infrastructure. Addressing these emerging threats will require unprecedented levels of international collaboration and innovative approaches to cybersecurity, with significant ramifications for national security policy and global economic stability. Overall, this research provides a timely and in-depth examination of the evolving cyber warfare tactics employed by state actors, underscoring the need for a fundamental reassessment of traditional security paradigms in an increasingly interconnected digital world.

Optimized Deep Neuro-Fuzzy Networks for Enhanced Cyber Forensic Analysis in Iot-Driven Big Data Ecosystems

The rapid expansion of Internet of Things (IoT) ecosystems has significantly increased the complexity and scale of cyber forensic investigations, necessitating innovative and efficient analytical methods. This study proposes an Optimized Deep Neuro-Fuzzy Network (DNFN) framework designed to enhance forensic capabilities in IoT-driven big data environments. The framework synergizes the feature extraction strengths of deep learning with the interpretative advantages of fuzzy logic, while employing optimization algorithms to fine-tune its parameters for improved performance. The DNFN framework addresses key challenges such as data heterogeneity, dynamic threat landscapes, and resource constraints in IoT ecosystems. Extensive experimentation on benchmark datasets demonstrates the framework's ability to achieve high accuracy in detecting cyber anomalies, reduce false positive rates, and support comprehensive forensic analysis. The proposed approach is a significant step toward enabling scalable, interpretable, and robust cyber forensic solutions tailored for the evolving complexities of IoT-driven networks.

AI Driven Cybersecurity

The advent of Artificial Intelligence (AI) has revolutionized the field of cybersecurity by introducing advanced mechanisms for detecting, preventing, and mitigating cyber threats. This research explores the intersection of AI and cybersecurity, highlighting the transformative potential of AI-driven solutions in combating increasingly sophisticated cyberattacks. By leveraging machine learning, deep learning, and neural network algorithms, AI enhances real-time threat detection, predictive analytics, and anomaly detection across diverse digital infrastructures. This study evaluates current AI-driven cybersecurity frameworks, emphasizing their efficacy in handling dynamic threat landscapes and addressing the limitations of traditional methods. Additionally, it examines ethical considerations, such as the potential misuse of AI by malicious actors and the need for transparent AI systems. Through comprehensive analysis, this research underscores the importance of developing resilient AI models to secure critical data and infrastructure in an era of rapidly evolving cyber risks. The findings provide actionable insights for policymakers, organizations, and technology developers, advocating for collaborative efforts to harness AI’s potential while addressing its inherent challenges.

UK Counter-Terrorism and Multiple and Complex Needs: A Policy-Informed Discourse Analysis

AbstractOf the four work strands of UK counter-terrorism (CT), Prevent and Pursue are directed towards stopping individuals from becoming involved in terrorism, and investigating and prosecuting those suspected of terrorist crimes. Over the past few years, both these two work strands of UK CT seem to have captured more individuals with Multiple and Complex Needs (MCN), especially people of young age and with mental health issues. This article describes key UK CT discourse on the terrorist threat landscape and its proposed countermeasures, with particular regard to the role of MCN. It considers the extent to which discourse on the threat and set-up of UK CT seems to correspond to academic research, media reporting and other open sources—and is interested in whether the apparent increase of MCN in UK CT (only) reflect real-life developments in the threat landscape, or whether the current structure of UK CT may disproportionately draw in individuals with MCN. It finds that while accurately capturing some recent trends, UK CT discourse overall reifies largely outdated ideas of the role of ideational radicalisation and of there being a sharp distinction between the categories of vulnerability and risk. It also suggests that the emphasis of UK CT seems to increasingly be on the category of risk and on the logic of Pursue—also when MCN are involved—rather than proposing a multifaceted approach to the ever-changing complexity of the threat of terrorism.

Cybersecurity strategies for non-profit organizations

The paper explores effective cybersecurity strategies tailored for non-profit organizations, addressing their unique challenges of limited resources and sensitive data handling. It examines the current threat landscape, highlighting common vulnerabilities such as phishing attacks, ransomware, and data breaches. The research presents key cybersecurity strategies, including risk assessment and management, employee training and awareness, implementation of technical controls, data protection and privacy measures, and third-party risk management. Cost-effective solutions are emphasized, focusing on open-source security tools, cloud-based services, and collaborative information-sharing networks. The study concludes that by adopting a holistic approach encompassing people, processes, and technology, non-profits can significantly enhance their security posture and resilience against cyber threats, ensuring the continuity of their critical missions while operating within resource constraints.

Impact of AI on the Cyber Kill Chain: A Systematic Review

The Cyber Kill Chain (CKC) defense model aims to assist subject matter experts in planning, identifying, and executing against cyber intrusion activity, by outlining seven stages required for adversaries to execute an attack. Recent advancements in Artificial Intelligence (AI) have empowered adversaries to execute sophisticated attacks to exploit system vulnerabilities. As a result, it is essential to consider how AI-based tools change the cyber threat landscape and affects the current standard CKC model. Thus, this study examines and categorizes how attackers use AI-based tools and offers potential defense mechanisms. We conducted a systematic literature review of 62 papers published between 2013 and 2023 from the Web of Science and Google Scholar databases. Our findings indicate that AI-based tools are used most effectively in the initial stages of cyberattacks. However, we find that current defence tools are not designed to counter these sophisticated attacks during these stages. Thus, we provide insights to 1) highlight the changing threat landscape due to AI and 2) to guide the development of cyberdefense mechanisms.

Enhancing intrusion detection in next-generation networks based on a multi-agent game-theoretic framework

<span lang="EN-US">With cyber threats becoming increasingly sophisticated, existing intrusion detection systems (IDS) in next generation networks (NGNs) are subjected to more false-positives and struggles to offer robust security feature, highlighting a critical need for more adaptive and reliable threat detection mechanisms. This research introduces a novel IDS that leverages a dueling deep Q-network (DQN) a reinforcement learning algorithm within game-theoretic framework simulating a multi-agent adversarial learning scenario to address these challenges. By employing a customized OpenAI Gym environment for realistic threat simulation and advanced dueling DQN mechanisms for reduced overestimation bias, the proposed scheme significantly enhances the adaptability and accuracy of intrusion detection. Comparative analysis against current state-of-the-art methods reveals that the proposed system achieves superior performance, with accuracy and F1-score improvements to 95.02% and 94.68%, respectively. These results highlight the potential scope of the proposed adaptive IDS to provide a robust defense against the dynamic threat landscape in NGNs.</span>

A Deep Learning Approach to Detecting Advanced Persistent Threats in Cybersecurity

Advanced Persistent Threats (APTs) represent one of the most sophisticated and insidious forms of cyber-attacks, often eluding traditional detection methods due to their stealthy and prolonged nature. This paper presents a novel approach to detecting APTs by leveraging the power of deep learning. We propose a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture both the spatial and temporal features inherent in APT behaviors. The model was trained and validated on a comprehensive dataset, demonstrating an accuracy of 98.5% in detecting APT activities, significantly outperforming traditional machine learning models. The proposed approach not only enhances detection accuracy but also reduces false positive rates, making it a robust solution for real-time cybersecurity applications. Our findings highlight the potential of deep learning to revolutionize APT detection, offering a scalable and adaptive framework for securing critical systems against evolving cyber threats. Future work will focus on refining the model for deployment in diverse operational environments and incorporating adaptive learning techniques to keep pace with the rapidly changing threat landscape

Best Practices for Database Security in the Age of AI

Database security has seen a dramatic change as a result of artificial intelligence's incorporation into contemporary technology, bringing with it previously unheard-of opportunities as well as obstacles. This thorough article looks at how businesses can use AI capabilities to safeguard sensitive data assets while fortifying their database security postures. With an emphasis on financial institutions and healthcare organizations, it looks at the changing threat landscape, crucial security measures, and practical implementation tactics across a range of sectors. The article further demonstrates the efficacy of threat detection systems, sophisticated encryption techniques, AI-powered security solutions, and thorough audit frameworks. It illustrates the vital significance of putting in place multi-layered security techniques that combine conventional safeguards with AI-enhanced protective measures through in-depth case studies and empirical analysis.

A hybrid DGA DefenseNet for detecting DGA domain names based on FastText and deep learning techniques

As technology rapidly advances, the Internet has become an essential part of modern life. However, public awareness of cybersecurity has not kept pace with the growing threat landscape. Cyber incidents have become more frequent, caused by factors such as malware, software vulnerabilities, and social engineering. With the evolution of malicious attack methods, there is a growing need for innovative and effective cybersecurity defense strategies. This study proposed a Domain Generation Algorithm (DGA) domains detection framework leveraging a deep learning architecture combined with FastText. Using FastText for word embedding extraction, this research developed the Hybrid DGA DefenseNet (HDDN), which integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This hybrid model extracts features from datasets and performs both detection and classification. On the Netlab360 and UMUDGA datasets, the model achieved detection accuracy of 97.70 % and 97.42 %, outperforming the Random Forest approach by 15.77 % and 16.29 %, and the C5.0 with GAN approach by 6.40 % and 7.22 %. Additionally, the model achieved classification accuracy of 93.86 % on the Netlab360 dataset and 90.09 % on the UMUDGA dataset, demonstrating the effectiveness of HDDN compared to existing methods.

Outcomes of Social Engineering: Understanding Cybercriminals' Exploitation of Human Psychology

This study explores the growing threat of social engineering attacks in India, examining how cybercriminals exploit human psychology to gain unauthorized access to sensitive information and systems. Analyzing data on compromised accounts, financial losses, and cybersecurity training adoption from 2014 to 2024, the research uncovers alarming trends - a steady rise in compromised accounts, strong correlations between account compromises and financial impacts, and a reactive, yet insufficient, response from organizations through increased training programs. The findings highlight the urgent need for a multi-faceted approach that combines technological safeguards with robust employee awareness initiatives to mitigate the human vulnerabilities exploited by social engineering tactics. Implications for policymakers, businesses, and individuals in India emphasize the critical importance of proactive cybersecurity measures to address this evolving threat landscape. Keywords: Social engineering, Cybercrime, Compromised accounts, Financial losses, Cybersecurity training, Human vulnerabilities

Beyond “Leaderless Resistance”? Toward a Typology for White Supremacist Extremist Leadership

The specific concept of white supremacist extremist leadership remains understudied, with significant implications for counterterrorism strategies. To bring greater clarity to the varied functions of white supremacist extremist leaders, I examined the role of key actors across the threat landscape. While the structure of hierarchical white supremacist extremist organizations lends itself to conventional approaches for group-based terrorist leadership analysis, underground white supremacist extremist networks and cells require a new framework for understanding influence in a non-hierarchical environment. To fill this gap, I propose a descriptive typology characterizing the distinct functions of underground white supremacist extremist leaders: Framers, Brokers, Enablers, and Pioneers.

Comprehensive Survey of Web Security Threats in 2024

In recent years, the landscape of web security threats has evolved rapidly, driven by advancements in technology and increasingly sophisticated attack vectors. This paper presents a comprehensive survey of prominent web security threats in 2024, examining both traditional vulnerabilities and emerging risks that have intensified in the digital environment. We explore a range of threats, including but not limited to application-layer attacks, network- layer intrusions, and data privacy concerns. The study draws upon a wealth of sources, including industry reports, academic research, and standards from leading organizations. Our findings underscore the critical need for web application developers, security professionals, and organizations to adopt proactive defense mechanisms and stay informed on current threats to effectively protect web-based assets. This survey aims to serve as a reference for security practitioners and researchers, highlighting essential vulnerabilities and encouraging further exploration of effective countermeasures. Keywords Web Security, Threat Detection, Data Privacy, Cross-Site Scripting (XSS), SQL Injection, DDoS Attacks, Zero-Trust Architecture, Quantum-Resistant Encryption, Cloud Security, Machine Learning in Security

Smart Security : A Comprehensive Exploration of AI and IoT in Cybersecurity

This paper investigates the transformative roles of Artificial Intelligence (AI) and the Internet of Things (IoT) in cybersecurity, especially as the complexity and connectivity of digital networks intensify. The rapid adoption of IoT devices in industries and homes alike has significantly broadened the cyber threat landscape, introducing countless points of vulnerability. Cybercriminals now target the vast and often unsecured data generated by IoT devices, exploiting weak access controls, outdated software, and inadequate encryption. In response, AI has become a critical tool in modern cybersecurity strategies, offering enhanced methods for threat detection, predictive analytics, and automated responses. AI-driven systems can proactively analyze patterns in network activity, detect anomalies, and anticipate future threats, all in real-time, providing a strong defence against increasingly sophisticated attacks. Additionally, AI facilitates automation in incident response, enabling immediate isolation or neutralization of threats before significant damage occurs. However, integrating IoT and AI in cybersecurity introduces new challenges, particularly concerning device interoperability, resource limitations in IoT devices, and privacy concerns due to the extensive data generated and processed. This paper explores these technologies in depth, examining their current applications, limitations, and the potential they hold for building smarter, more resilient cybersecurity frameworks in the future.

The Rise of AI-powered Cybercrime: a Data-driven Analysis of Emerging Threats

The integration of artificial intelligence (AI) in cybercrime represents a transformative shift in the global threat landscape, introducing unprecedented challenges to cybersecurity frameworks. This comprehensive article examines the dramatic 238% surge in AI-powered cyber attacks during 2023, resulting in global damages exceeding $8.5 billion. Through analysis of 100,000 malware samples, 50,000 documented incidents, and 2,500 attack patterns across Fortune 500 companies, this study quantifies the impact of AI integration in cyber attacks. Key findings reveal that AI-enhanced attacks achieve a 67% higher success rate while reducing operational complexity by 72%. The article demonstrates concerning trends across four critical domains: automated attacks, AI-enhanced phishing (showing an evolution from 2.9% to 8.7% success rate), malware evolution, and deepfake-based fraud. The article also presents actionable mitigation strategies, including AI-based security solutions that demonstrate a 97.8% improvement in response efficiency, reducing incident response times from 6 hours to 8 minutes. This article provides a framework for organizations to strengthen their security posture against emerging AI-powered threats through systematic implementation of advanced defense mechanisms and strategic investment in AI-enhanced security solutions.

From Firewall to AI: Strengthening Linux Server Security

Nowadays, cybersecurity issues are constantly increasing. Some of the main challenges are the rapidly evolving threat landscape, the lack of skilled cybersecurity professionals, complexity of IT environments and emerging technologies, Zero-Day vulnerabilities, lack of international cooperation, user behavior and social engineering. The selection of an operating system can significantly influence the establishment of robust defenses against cyber threats. Now, many strategies are known for defense-in-depth of critical server infrastructure, but the ever-increasing sophistication of cyber-attacks must also be considered. When a previously unknown attack occurs, a defense system must be able to recognize it and act. The current development of Artificial Intelligence (AI) makes it possible to create this type of intelligent protection. This paper aims to provide an overview of some of the most renowned traditional practices and explores the potential of leveraging a neural network model to enhance cybersecurity used to protect Linux servers against cyber-attacks.

Zero Trust VPN (ZT-VPN): A Systematic Literature Review and Cybersecurity Framework for Hybrid and Remote Work

Modern organizations have migrated from localized physical offices to work-from-home environments. This surge in remote work culture has exponentially increased the demand for and usage of Virtual Private Networks (VPNs), which permit remote employees to access corporate offices effectively. However, the technology raises concerns, including security threats, latency, throughput, and scalability, among others. These newer-generation threats are more complex and frequent, which makes the legacy approach to security ineffective. This research paper gives an overview of contemporary technologies used across enterprises, including the VPNs, Zero Trust Network Access (ZTNA), proxy servers, Secure Shell (SSH) tunnels, the software-defined wide area network (SD-WAN), and Secure Access Service Edge (SASE). This paper also presents a comprehensive cybersecurity framework named Zero Trust VPN (ZT-VPN), which is a VPN solution based on Zero Trust principles. The proposed framework aims to enhance IT security and privacy for modern enterprises in remote work environments and address concerns of latency, throughput, scalability, and security. Finally, this paper demonstrates the effectiveness of the proposed framework in various enterprise scenarios, highlighting its ability to prevent data leaks, manage access permissions, and provide seamless security transitions. The findings underscore the importance of adopting ZT-VPN to fortify cybersecurity frameworks, offering an effective protection tool against contemporary cyber threats. This research serves as a valuable reference for organizations aiming to enhance their security posture in an increasingly hostile threat landscape.

Innovations in Cloud Database Security: Addressing Emerging Threats

This article examines the latest innovations in cloud database security, addressing the challenges posed by an increasingly sophisticated threat landscape. As organizations rapidly adopt cloud databases, they face expanding attack surfaces and evolving cyber threats, including AI-powered attacks and quantum computing risks. The article explores cutting-edge security measures such as quantum-resistant encryption, homomorphic encryption, zero-trust architectures, and AI-driven threat detection systems. It also discusses the integration of security into database development processes through DevSecOps practices. The article highlights the importance of a multi-faceted approach to cloud database security, combining advanced technologies with proactive strategies. By analyzing recent advancements and their impacts, this article provides insights into the future of cloud database security and offers guidance for organizations seeking to protect their sensitive data in complex cloud environments.