Bappa Muktar^*, Vincent Fono, Adama Nouboukpo

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4705-4727, 2025, DOI:10.32604/cmc.2025.067733 - 23 October 2025

Abstract Vehicular Ad Hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), especially for real-time communication involving emergency vehicles. Yet, Distributed Denial of Service (DDoS) attacks can disrupt safety-critical channels and undermine reliability. This paper presents a robust, scalable framework for detecting DDoS attacks in highway VANETs. We construct a new dataset with Network Simulator 3 (NS-3) and Simulation of Urban Mobility (SUMO), enriched with real mobility traces from Germany’s A81 highway (OpenStreetMap). Three traffic classes are modeled: DDoS, Voice over IP (VoIP), and Transmission Control Protocol Based (TCP-based) video streaming (VideoTCP). The pipeline includes normalization,… More >

Xuezhi Wen^1,2, Eric Danso^1,2,*, Solomon Danso¹

Journal of Cyber Security, Vol.7, pp. 439-462, 2025, DOI:10.32604/jcs.2025.070587 - 17 October 2025

Abstract Cloud-based intrusion detection systems increasingly face sophisticated adversarial attacks such as evasion and poisoning that exploit vulnerabilities in traditional machine learning (ML) models. While deep learning (DL) offers superior detection accuracy for high-dimensional cloud logs, it remains vulnerable to adversarial perturbations and lacks interpretability. Conversely, Hidden Markov Models (HMMs) provide probabilistic reasoning but struggle with raw, sequential cloud data. To bridge this gap, we propose a Deep Learning-Enhanced Ensemble Hidden Markov Model (DL-HMM) framework that synergizes the strengths of Long Short-Term Memory (LSTM) networks and HMMs while incorporating adversarial training and ensemble learning. Our architecture… More >

Shengjia Chang, Baojiang Cui^*, Shaocong Feng

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3805-3827, 2025, DOI:10.32604/cmes.2025.067756 - 30 September 2025

Abstract With the rapid advancement of mobile communication networks, key technologies such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats. Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors. To overcome these challenges, this paper presents a novel algorithmic framework, SD-5G, designed for high-precision intrusion detection in 5G environments. SD-5G adopts a three-stage architecture comprising traffic feature extraction, elastic representation, and adaptive classification. Specifically, an enhanced Concrete… More >

Fatma S. Alrayes¹, Mohammed Zakariah^2,*, Mohammed K. Alzaylaee³, Syed Umar Amin⁴, Zafar Iqbal Khan⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3457-3484, 2025, DOI:10.32604/cmc.2025.068599 - 23 September 2025

Abstract Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information. The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks. The WUSTL-EHMS 2020 dataset trains and evaluates the model, constituting an imbalanced class distribution (87.46% normal traffic and 12.53% intrusion attacks). To address this imbalance, the study balances the effect of training Bias through Stratified K-fold cross-validation (K = 5), so that… More >

Hoon Ko¹, Marek R. Ogiela², Libor Mesicek³, Sangheon Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2985-2997, 2025, DOI:10.32604/cmc.2025.065885 - 23 September 2025

Abstract The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the More >

Junzhe Cheng^*

Journal of Cyber Security, Vol.7, pp. 343-357, 2025, DOI:10.32604/jcs.2025.067979 - 22 September 2025

Abstract We present a novel Transformer-based network intrusion detection system (IDS) that automatically learns complex feature relationships from raw traffic. Our architecture embeds both categorical (e.g., protocol, flag) and numerical (e.g., packet count, duration) inputs into a unified latent space with positional encodings, and processes them through multi-layer multi-head self-attention blocks. The Transformer’s global attention enables the IDS to capture subtle, long-range correlations in the data (e.g., coordinated multi-step attacks) without manual feature engineering. We complement the model with extensive data augmentation (SMOTE, GANs) to mitigate class imbalance and improve robustness. In evaluation on benchmark datasets… More >

Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal², Heba G. Mohamed³, Muhammad Sheraz⁴, Teong Chee Chuah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2647-2681, 2025, DOI:10.32604/cmes.2025.068849 - 31 August 2025

Abstract The Internet of Things (IoT) ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027, operating in distributed networks with resource limitations and diverse system architectures. The current conventional intrusion detection systems (IDS) face scalability problems and trust-related issues, but blockchain-based solutions face limitations because of their low transaction throughput (Bitcoin: 7 TPS (Transactions Per Second), Ethereum: 15–30 TPS) and high latency. The research introduces MBID (Multi-Tier Blockchain Intrusion Detection) as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection, which… More >

Saad Awadh Alanazi¹, Fahad Ahmad^2,3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 769-800, 2025, DOI:10.32604/cmc.2025.066753 - 29 August 2025

Abstract This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to… More >

Kuncham Sreenivasa Rao¹, Rajitha Kotoju², B. Ramana Reddy³, Taher Al-Shehari⁴, Nasser A. Alsadhan⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1899-1916, 2025, DOI:10.32604/cmc.2025.064728 - 29 August 2025

Abstract Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things (SCADA-IIoT) systems against intruders has become essential since industrial control systems now oversee critical infrastructure, and cyber attackers more frequently target these systems. Due to their connection of physical assets with digital networks, SCADA-IIoT systems face substantial risks from multiple attack types, including Distributed Denial of Service (DDoS), spoofing, and more advanced intrusion methods. Previous research in this field faces challenges due to insufficient solutions, as current intrusion detection systems lack the necessary accuracy, scalability, and adaptability needed for IIoT environments. This paper introduces CyberFortis, a… More >

Muhammad Usama¹, Arshad Aziz², Imtiaz Hassan², Shynar Akhmetzhanova³, Sultan Noman Qasem^4,*, Abdullah M. Albarrak⁴, Tawfik Al-Hadhrami⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1225-1248, 2025, DOI:10.32604/cmes.2025.067098 - 31 July 2025

Abstract The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges, particularly in safeguarding sensitive patient data and maintaining the integrity of medical services. As healthcare becomes more data-driven, cyberattacks targeting these systems continue to rise, necessitating the development of robust, domain-adapted Intrusion Detection Systems (IDS). However, current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare. To address this gap, this study introduces HCKDDCUP, a synthetic dataset modeled on the widely used KDDCUP benchmark, augmented with healthcare-relevant attributes such as patient data, treatments, and… More >