Tong Wu, Dawei Zhou, Qingyu Ou^*, Fang Luo

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073482 - 12 January 2026

Abstract The increasing interconnection of modern industrial control systems (ICSs) with the Internet has enhanced operational efficiency, but also made these systems more vulnerable to cyberattacks. This heightened exposure has driven a growing need for robust ICS security measures. Among the key defences, intrusion detection technology is critical in identifying threats to ICS networks. This paper provides an overview of the distinctive characteristics of ICS network security, highlighting standard attack methods. It then examines various intrusion detection methods, including those based on misuse detection, anomaly detection, machine learning, and specialised requirements. This paper concludes by exploring More >

MD Hamid Borkot Tulla^1,*, MD Moniur Rahman Ratan², Rashid MD Mamunur³, Abdullah Hil Safi Sohan⁴, MD Matiur Rahman⁵

Journal of Cyber Security, Vol.7, pp. 675-691, 2025, DOI:10.32604/jcs.2025.074737 - 24 December 2025

Abstract Phishing is considered one of the most widespread cybercrimes due to the fact that it combines both technical and human vulnerabilities with the intention of stealing sensitive information. Traditional blacklist and heuristic-based defenses fail to detect such emerging attack patterns; hence, intelligent and transparent detection systems are needed. This paper proposes an explainable machine learning framework that integrates predictive performance with regulatory accountability. Four models were trained and tested on a balanced dataset of 10,000 URLs, comprising 5000 phishing and 5000 legitimate samples, each characterized by 48 lexical and content-based features: Decision Tree, XGBoost, Logistic… More >

Yu Yan¹, Yu Yang^1,*, Shen Fang¹, Minna Gao², Yiding Chen¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 875-896, 2024, DOI:10.32604/cmc.2024.051685 - 18 July 2024

Abstract In the face of the effective popularity of the Internet of Things (IoT), but the frequent occurrence of cybersecurity incidents, various cybersecurity protection means have been proposed and applied. Among them, Intrusion Detection System (IDS) has been proven to be stable and efficient. However, traditional intrusion detection methods have shortcomings such as low detection accuracy and inability to effectively identify malicious attacks. To address the above problems, this paper fully considers the superiority of deep learning models in processing high-dimensional data, and reasonable data type conversion methods can extract deep features and detect classification using… More >