Fatma S. Alrayes¹, Syed Umar Amin^2,*, Nada Ali Hakami², Mohammed K. Alzaylaee³, Tariq Kashmeery⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 581-614, 2025, DOI:10.32604/cmes.2025.065188 - 31 July 2025

Abstract The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks, there is a demand for better techniques to improve detection reliability. This study introduces a new method, the Deep Adaptive Multi-Layer Attention Network (DAMLAN), to boost the result of intrusion detection on network data. Due to its multi-scale attention mechanisms and graph features, DAMLAN aims to address both known and unknown intrusions. The real-world NSL-KDD dataset, a popular choice among IDS researchers, is used to… More >

Tony Thomas^*, Ravi Prakash, Soumya Pal

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4055-4104, 2025, DOI:10.32604/cmc.2025.064903 - 30 July 2025

Abstract The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale… More >

Yuan Feng¹, Yajie Si², Jianwei Zhang^3,4,*, Zengyu Cai^5,*, Hongying Zhao⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4431-4449, 2025, DOI:10.32604/cmc.2025.064696 - 30 July 2025

Abstract With the rapid development of the industrial Internet, the network security environment has become increasingly complex and variable. Intrusion detection, a core technology for ensuring the security of industrial control systems, faces the challenge of unbalanced data samples, particularly the low detection rates for minority class attack samples. Therefore, this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network (SA-WGAN) to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios. The proposed method integrates a self-attention mechanism… More >

Atena Shiranzaei^1,*, Emad Alizadeh², Mahdi Rabbani³, Sajjad Bagheri Baba Ahmadi^4,*, Mohsen Tajgardan⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5381-5402, 2025, DOI:10.32604/cmc.2025.064414 - 30 July 2025

Abstract The sinkhole attack is one of the most damaging threats in the Internet of Things (IoT). It deceptively attracts neighboring nodes and initiates malicious activity, often disrupting the network when combined with other attacks. This study proposes a novel approach, named NADSA, to detect and isolate sinkhole attacks. NADSA is based on the RPL protocol and consists of two detection phases. In the first phase, the minimum possible hop count between the sender and receiver is calculated and compared with the sender’s reported hop count. The second phase utilizes the number of DIO messages to More >

Xiaogang Yuan^*, Huan Pei, Yanlin Wu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5555-5575, 2025, DOI:10.32604/cmc.2025.063537 - 30 July 2025

Abstract In the complex environment of Wireless Sensor Networks (WSNs), various malicious attacks have emerged, among which internal attacks pose particularly severe security risks. These attacks seriously threaten network stability, data transmission reliability, and overall performance. To effectively address this issue and significantly improve intrusion detection speed, accuracy, and resistance to malicious attacks, this research designs a Three-level Intrusion Detection Model based on Dynamic Trust Evaluation (TIDM-DTE). This study conducts a detailed analysis of how different attack types impact node trust and establishes node models for data trust, communication trust, and energy consumption trust by focusing… More >

Manzheng Yuan^1,2, Kai Yang^2,*

Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025

Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >

Khadija Bouzaachane^1,*, El Mahdi El Guarmah², Abdullah M. Alnajim³, Sheroz Khan⁴

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2391-2410, 2025, DOI:10.32604/cmc.2025.065031 - 03 July 2025

Abstract The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2… More >

Hui Xu, Wei Huang^*, Longtan Bai

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3053-3073, 2025, DOI:10.32604/cmc.2025.064930 - 03 July 2025

Abstract With the birth of Software-Defined Networking (SDN), integration of both SDN and traditional architectures becomes the development trend of computer networks. Network intrusion detection faces challenges in dealing with complex attacks in SDN environments, thus to address the network security issues from the viewpoint of Artificial Intelligence (AI), this paper introduces the Crayfish Optimization Algorithm (COA) to the field of intrusion detection for both SDN and traditional network architectures, and based on the characteristics of the original COA, an Improved Crayfish Optimization Algorithm (ICOA) is proposed by integrating strategies of elite reverse learning, Levy flight,… More >

Mohammed S. Alshehri^1,*, Oumaima Saidani², Wajdan Al Malwi³, Fatima Asiri³, Shahid Latif ⁴, Aizaz Ahmad Khattak⁵, Jawad Ahmad⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3899-3920, 2025, DOI:10.32604/cmes.2025.064874 - 30 June 2025

Abstract The emergence of Generative Adversarial Network (GAN) techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems (IDS). However, conventional GAN-based IDS models face several challenges, including training instability, high computational costs, and system failures. To address these limitations, we propose a Hybrid Wasserstein GAN and Autoencoder Model (WGAN-AE) for intrusion detection. The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model. The model was trained and evaluated using two recent benchmark datasets, 5GNIDD and IDSIoT2024. When trained on the 5GNIDD dataset,… More >

Alireza Bahmani^*

Journal on Internet of Things, Vol.7, pp. 1-17, 2025, DOI:10.32604/jiot.2025.060786 - 24 June 2025

Abstract With the development of the Internet of Things (IoT) technology and its widespread integration in various aspects of life, the risks associated with cyberattacks on these systems have increased significantly. Vulnerabilities in IoT devices, stemming from insecure designs and software weaknesses, have made attacks on them more complex and dangerous compared to traditional networks. Conventional intrusion detection systems are not fully capable of identifying and managing these risks in the IoT environment, making research and evaluation of suitable intrusion detection systems for IoT crucial. In this study, deep learning, multi-layer perceptron (MLP), Random Forest (RF),… More >