Bala Thripura Akasam^*

Journal of Cyber Security, Vol.8, pp. 525-539, 2026, DOI:10.32604/jcs.2026.085438 - 21 August 2026

Abstract The widespread adoption of Artificial Intelligence (AI) coding assistants across enterprise software development teams has accelerated delivery velocity while simultaneously introducing a persistent and empirically documented security quality gap in the code these tools produce. Vulnerability classes including insecure output handling, prompt injection constructs, sensitive information disclosure patterns, and cryptographic misuse appear at elevated rates in AI-generated output regardless of model advancement, while organizational governance frameworks have failed to keep pace with the speed of AI tool deployment, creating conditions in which vulnerable code reaches production through informal risk acceptance rather than accountable remediation processes.… More >

Praise Emeka Nze^*, Adeniran Kolade Ademuwagun, Muktar Bello, Fortune Daberechi Ifeanyi, Samaila Musa Abdullahi, John Tighil

Journal of Cyber Security, Vol.8, pp. 469-486, 2026, DOI:10.32604/jcs.2026.083943 - 21 August 2026

Abstract The rapid evolution of adversarial cyber threats demands proactive, scalable security testing methodologies capable of producing realistic, organization-specific attack scenarios. Conventional approaches, including manual red-teaming, scripted Breach and Attack Simulation (BAS) platforms, and tabletop exercises, are constrained by high expert dependency, limited scenario variability, and an inability to dynamically adapt to an organization’s unique threat profile. This paper proposes and evaluates a Large Language Model (LLM)-Assisted Threat-Driven Testing System that integrates the MITRE Adversarial Tactics, Techniques, and Common Knowledge (MITRE ATT&CK) framework v14, a structured knowledge base of adversarial tactics, techniques, and procedures (TTPs), with… More >

Darlington Okeke^*

Journal of Cyber Security, Vol.8, pp. 487-523, 2026, DOI:10.32604/jcs.2026.080111 - 21 August 2026

Abstract Background: Novel cyber threats to organizations have greatly escalated due to the high digitalization rates of organizations, and cybersecurity readiness is a critical capability of organizations and not a technical issue. Although there is increased awareness, most organizations are ill-equipped due to weaknesses in human behavior, governance, leadership commitment, technology infrastructure, and incident response mechanisms. This paper discusses organizational factors that play a major role in cybersecurity readiness. Methods: Primary data from 230 participants were collected using a questionnaire approach and analyzed using IBM SPSS software. The quantitative methods adopted include descriptive statistics, Cronbach’s reliability More >

Ali Hassan¹, Syed Rizwan Hassan^2,*, Ammar Rafiq³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084672 - 13 August 2026

Abstract Artificial Intelligence of Things (AIoT) systems have emerged through the rapid integration of artificial intelligence (AI) and the Internet of Things (IoT), enabling intelligent sensing, distributed learning, and real-time decision-making across diverse application domains. However, this convergence also introduces a significantly expanded adversarial attack surface spanning sensing devices, communication networks, learning pipelines, and actuation environments. This paper presents a comprehensive systematic review of adversarial threats and defence mechanisms in AIoT systems using a novel 3D-AIoT-TT (Three-Dimensional AIoT Threat Taxonomy) framework. The proposed taxonomy jointly models three fundamental dimensions: (i) AI pipeline stages, (ii) IoT architectural… More >

Maher Alharby^1,*, Ali Alssaiari^2,3

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084639 - 13 August 2026

Abstract Large Language Models (LLMs) are increasingly being used to generate source code. However, a substantial proportion of their output contains security vulnerabilities. Existing defenses typically apply uniform analysis to all code fragments, irrespective of their risk profiles. This study presents RAVE-Code, a three-layer framework that calibrates the verification effort based on the risk associated with each detected weakness. The Detection layer employs Bandit for pattern-based static analysis, annotating findings with their respective Common Weakness Enumeration (CWE) classes. The Risk Scoring layer calculates a composite risk score for each weakness instance by integrating the Common Vulnerability… More >

Muhammad Mujahid¹, Fatima Alshannaq¹, Shaha Al-Otaibi², Tanzila Saba^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084273 - 13 August 2026

Abstract Cybersecurity threats in Internet of Things (IoT) networks have escalated, enabled by rapid advancements in wireless communication and edge computing technologies. These advancements expose networks to a wide range of sophisticated and evolving threats and increasingly complex research challenges. Traditional Intrusion Detection and Prevention Systems (IDS/IPS) often fail to provide reliable performance regarding the flexibility and scalability required to handle evolving attack patterns. This study proposes an APENet approach to detect cyberattacks from a real-world cybersecurity dataset, and incorporated a contextual dependency mechanism. The approach captures both local transition dependencies and global relational interactions within… More >

Tuan Nguyen Kim^1,*, Son Doan Trung¹, Nguyen Minh Nhut Pham²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084268 - 13 August 2026

Abstract Static Windows Portable Executable (PE) malware detection remains a significant challenge due to the growing use of packing, obfuscation, and code reuse techniques, which gradually reduce the effectiveness of signature-based and manually engineered feature approaches. Recent deep learning models that operate directly on binary code or static features have achieved encouraging results; however, most still rely on global file-level representations. Such approaches are susceptible to noise introduced by padding or obfuscation and may overlook localized malicious regions. Moreover, many multi-view methods process different feature sources independently, lacking mechanisms to enforce semantic consistency across views. This… More >

Yuh-Shihng Chang¹, Hsuan-Chao Huang^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083954 - 13 August 2026

Abstract The integration of Building Information Modeling (BIM), Internet of Things (IoT), digital twins, and artificial intelligence (AI) has enabled advanced smart building operations with real-time monitoring and data driven decision-making capabilities. However, existing systems still face critical challenges in ensuring trusted data provenance, secure cross-layer data exchange, and robust access control in distributed IoT environments. These limitations significantly affect the reliability and trustworthiness of data driven analytics and system-level decision-making. To address these challenges, this study proposes a blockchain-assisted BIM–IoT digital twin architecture for trusted operational risk prediction in smart buildings. The proposed framework integrates… More >

Tahani Alsubait^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083550 - 13 August 2026

Abstract The recent sophistication of contemporary cyber threats, such as advanced persistent threats (APTs), zero-day exploits, and polymorphic malware, has revealed serious limitations of traditional rule-based and shallow machine learning detection systems. This paper introduces a new self-managed cyber threat detection and response model, CyberSentinel-LLM, that leverages a fine-tuned large language model (LLM) and a multi-agent reinforcement learning system. The framework employs a LoRA-adapted LLaMA-3-8B backbone (fine-tuned on domain-specific cybersecurity log data using Low-Rank Adaptation with rank r = 16) for contextual log analysis, semantic threat classification, and automated incident response through four specialised agents: Detection,… More >

Alanoud Al Mazroa¹, Abdulrahman Mohammed Alamoudi², Nurdaulet Karabayev³, Jawad Ahmad^4,*, Muhammad Shahbaz Khan⁵

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083381 - 13 August 2026

Abstract The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these… More >