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 >

Akhila Reddy Yadulla, Mounica Yenugula, Vinay Kumar Kasula^*, Bhargavi Konda, Bala Yashwanth Reddy Thumma

Journal on Internet of Things, Vol.7, pp. 19-48, 2025, DOI:10.32604/jiot.2025.062733 - 11 July 2025

Abstract This study systematically reviews the Internet of Things (IoT) security research based on literature from prominent international cybersecurity conferences over the past five years, including ACM Conference on Computer and Communications Security (ACM CCS), USENIX Security, Network and Distributed System Security Symposium (NDSS), and IEEE Symposium on Security and Privacy (IEEE S&P), along with other high-impact studies. It organizes and analyzes IoT security advancements through the lenses of threats, detection methods, and defense strategies. The foundational architecture of IoT systems is first outlined, followed by categorizing major threats into eight distinct types and analyzing their More >

Mathew Nicho^1,2,*, Oluwasegun Adelaiye³, Christopher D. McDermott⁴, Shini Girija⁵

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 281-308, 2025, DOI:10.32604/cmc.2025.059597 - 26 March 2025

Abstract The successful penetration of government, corporate, and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace. Advanced Persistent Threat (APT) attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence (AI)-based defense mechanisms. While AI has enhanced organizational capabilities for deterrence, detection, and mitigation of APTs, the global escalation in reported incidents, particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology (IT) security administrators and decision-makers. Literature review has identified the stealthy lateral movement (LM) of malware within the initially… More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024

Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… More >