Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

Gang Xu^1,2, Lefeng Wang¹, Yuwei Huang², Yong Lu³, Xin Liu⁴, Weijie Tan⁵, Zongpeng Li⁶, Xiu-Bo Chen^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074256 - 10 February 2026

Abstract The Intrusion Detection System (IDS) is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities. Clustering algorithms are often incorporated into IDS; however, conventional clustering-based methods face notable drawbacks, including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions. To overcome the performance limitations of existing methods, this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm (SC-QGA) and an improved quantum artificial bee colony algorithm hybrid K-means (IQABC-K). First, the SC-QGA algorithm is constructed based on… More >

Noura Mohammed Alaskar¹, Muzammil Hussain², Saif Jasim Almheiri¹, Atta-ur-Rahman³, Adnan Khan^4,5,6, Khan M. Adnan^7,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074041 - 10 February 2026

Abstract The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats. The early detection of threats is both necessary and complex, yet these interconnected healthcare settings generate enormous amounts of heterogeneous data. Traditional Intrusion Detection Systems (IDS), which are generally centralized and machine learning-based, often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy. Moreover, traditional AI-driven IDS usually face challenges in handling large-scale, heterogeneous healthcare data while ensuring data… More >

Qasem Abu Al-Haija^1,*, Shahad Al Tamimi²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073540 - 10 February 2026

Abstract Adversarial Reinforcement Learning (ARL) models for intelligent devices and Network Intrusion Detection Systems (NIDS) improve system resilience against sophisticated cyber-attacks. As a core component of ARL, Adversarial Training (AT) enables NIDS agents to discover and prevent new attack paths by exposing them to competing examples, thereby increasing detection accuracy, reducing False Positives (FPs), and enhancing network security. To develop robust decision-making capabilities for real-world network disruptions and hostile activity, NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity. The accuracy and timeliness of… More >

Kirubavathi Ganapathiyappan¹, Chahana Ravikumar¹, Raghul Alagunachimuthu Ranganayaki¹, Ayman Altameem², Ateeq Ur Rehman^3,*, Ahmad Almogren^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072840 - 10 February 2026

Abstract Android smartphones have become an integral part of our daily lives, becoming targets for ransomware attacks. Such attacks encrypt user information and ask for payment to recover it. Conventional detection mechanisms, such as signature-based and heuristic techniques, often fail to detect new and polymorphic ransomware samples. To address this challenge, we employed various ensemble classifiers, such as Random Forest, Gradient Boosting, Bagging, and AutoML models. We aimed to showcase how AutoML can automate processes such as model selection, feature engineering, and hyperparameter optimization, to minimize manual effort while ensuring or enhancing performance compared to traditional… More >

Sooyong Jeong^1,#, Cheolhee Park^2,#, Dowon Hong^3,*, Changho Seo⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072561 - 10 February 2026

Abstract With the growing complexity and decentralization of network systems, the attack surface has expanded, which has led to greater concerns over network threats. In this context, artificial intelligence (AI)-based network intrusion detection systems (NIDS) have been extensively studied, and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms. However, most existing works focus on individual distributed learning frameworks, and there is a lack of systematic evaluations that compare different algorithms under consistent conditions. In this paper, we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning (FL), Split… More >

Fatima Al-Quayed^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074799 - 29 January 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative, distributed architectural solutions. This paper proposes FE-ACS (Fog-Edge Adaptive Cybersecurity System), a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge, fog, and cloud layers to optimize security efficacy, latency, and privacy. Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923, while maintaining significantly lower end-to-end latency (18.7 ms) compared to cloud-centric (152.3 ms) and fog-only (34.5… More >

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 >

Hanaa Nafea¹, Awais Qasim², Sana Abdul Sattar², Adeel Munawar³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072281 - 12 January 2026

Abstract The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats, making intrusion detection a critical aspect of ensuring their secure operation. Traditional intrusion detection systems have limitations in terms of centralized architecture, lack of transparency, and vulnerability to single points of failure. This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems. This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signature-based intrusion detection system. The introduced More >

Haohui Su¹, Xuan Zhang^1,*, Lvjun Zheng¹, Xiaojie Shen², Hua Liao¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072093 - 12 January 2026

Abstract Distributed Denial-of-Service (DDoS) attacks pose severe threats to Industrial Control Networks (ICNs), where service disruption can cause significant economic losses and operational risks. Existing signature-based methods are ineffective against novel attacks, and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments. To address these challenges, this study proposes a deep feature-driven hybrid framework that integrates Transformer, BiLSTM, and KNN to achieve accurate and robust DDoS detection. The Transformer component extracts global temporal dependencies from network traffic flows, while BiLSTM captures fine-grained sequential dynamics. The learned… More >