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 >

Malika Abid¹, Mohammed Kamel Benkaddour¹, Mohamed Benouis², Amine Khaldi¹, Monalisa Sahu³, Aditya Kumar Sahu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081055 - 27 July 2026

Abstract Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with… More >

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 >