Marwan Ali Albahar^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079941 - 08 May 2026

Abstract Advanced Persistent Threats (APTs) are stealthy cyberattacks that can evade detection in system-level audit logs. Provenance graphs encode these logs as interacting entities and events, exposing a causal and dependency structure that is often obscured in linear representations. Prior provenance-based detectors typically apply anomaly detection over such graphs, yet they frequently incur high false-positive rates and produce coarse grained alerts; moreover, approaches that heavily depend on node-specific identifiers (e.g., file paths) can learn spurious correlations, reducing robustness and limiting reliability across heterogeneous workloads. In this paper, we present Self-Training Adaptive Graph Encoder (stage), a lightweight, self-supervised… More >

Anil Kumar¹, Abhay Bhatia¹, Amit Singh², Preeti Rani^3,*, Vincent Omollo Nyangaresi^4,*, Mahendihasan S. Heera⁵

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076553 - 08 May 2026

Abstract The Internet of Things (IoT) enables seamless real-time monitoring and data exchange across distributed and heterogeneous environments with wireless sensor networks (WSNs). The open architecture and resource constraints of wireless sensor networks (WSNs) make them highly vulnerable to internal security threats caused by malicious or compromised nodes, particularly in Internet of Things (IoT) environments. To address this issue, we proposed Dynamic Trust Evaluation Model (DTEM), designed to provide a secure, scalable, and efficient framework for IoT-based WSNs. The proposed model identifies the role of trust management in routing, data aggregation, and intrusion detection, including trust-based… More >

Hamed Alqahtani¹, Gulshan Kumar^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077367 - 09 April 2026

Abstract Large Language Models (LLMs) are becoming integral components of modern cybersecurity ecosystems, simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence–Generated Content (AIGC)-driven threats. This PRISMA-guided systematic review synthesises 167 peer-reviewed studies published between 2022 and 2025 and proposes a unified threat–defence–evaluation taxonomy as a central analytical framework to consolidate a previously fragmented body of research. Guided by this taxonomy, the review first examines AIGC-enabled threats, including automated and highly personalised phishing, polymorphic malware and exploit generation, jailbreak and adversarial prompting, prompt-injection attack vectors, multimodal deception, persona-steering attacks, and large-scale… More >

Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Khalid Haseeb¹, Imran Qureshi^2,*, Naveed Abbas¹, Muhammad Ali³, Muhammad Arif Shah⁴, Qaisar Abbas²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4349-4362, 2025, DOI:10.32604/cmes.2025.072326 - 23 December 2025

Abstract The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems (CPS-IoT) for real-time monitoring, intelligent decision-making, and efficient resource management, particularly in intelligent transportation and vehicular networks. Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic, data-intensive, and vehicular environments. However, challenges such as high computational overhead, uneven load distribution, and inefficient utilization of communication resources significantly hinder scalability and responsiveness. Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems. Distributed computing for More >

Boniface Wambui^1,*, Margaret Mwinji¹, Hellen Nyambura²

Journal of Cyber Security, Vol.7, pp. 463-482, 2025, DOI:10.32604/jcs.2025.069444 - 23 October 2025

Abstract As universities rapidly embrace digital transformation, their growing dependence on interconnected systems for academic, research, and administrative operations has significantly heightened their exposure to sophisticated cyber threats. Traditional defenses such as firewalls and signature-based intrusion detection systems have proven inadequate against evolving attacks like malware, phishing, ransomware, and advanced persistent threats (APTs). This growing complexity necessitates intelligent, adaptive, and anticipatory cybersecurity strategies. Artificial Intelligence (AI) offers a transformative approach by enabling automated threat detection, anomaly identification, and real-time incident response. This study sought to design and evaluate an AI-driven cybersecurity framework specifically for university networks… More >

Abrar M. Alajlan^1,*, Marwah M. Almasri²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5237-5262, 2025, DOI:10.32604/cmc.2025.066736 - 23 October 2025

Abstract Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats. Attackers can non-invasively manipulate sensors and spoof controllers, which in turn increases the autonomy of the system. Even though the focus on protecting against sensor attacks increases, there is still uncertainty about the optimal timing for attack detection. Existing systems often struggle to manage the trade-off between latency and false alarm rate, leading to inefficiencies in real-time anomaly detection. This paper presents a framework designed to monitor, predict, and control dynamic systems with a particular emphasis on detecting and adapting to… More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

Adam Khalid¹, Anazida Zainal¹, Fuad A. Ghaleb², Bander Ali Saleh Al-rimy³, Yussuf Ahmed^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5665-5691, 2025, DOI:10.32604/cmc.2025.063451 - 30 July 2025

Abstract Advanced Persistent Threats (APTs) represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour, long-term persistence, and ability to bypass traditional detection systems. The complexity of real-world network data poses significant challenges in detection. Machine learning models have shown promise in detecting APTs; however, their performance often suffers when trained on large datasets with redundant or irrelevant features. This study presents a novel, hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data. It combines Mutual Information (MI), Symmetric… More >

Josh McNett¹, Jackie McNett^2,*

Journal of Cyber Security, Vol.7, pp. 279-306, 2025, DOI:10.32604/jcs.2025.067514 - 25 July 2025

Abstract This review explores the implementation and effectiveness of continuous evaluation programs in managing and mitigating insider threats within organizations. Continuous evaluation programs involve the ongoing assessment of individuals’ suitability for access to sensitive information and resources by monitoring their behavior, access patterns, and other indicators in real-time. The review was conducted using a comprehensive search across various academic and professional databases, including IEEE Xplore, SpringerLink, and Google Scholar and papers were selected from a time span of 2015–2023. The review outlines the importance of defining the scope and objectives of such programs, which should include… More >