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 >

Runzhang Liang^1,2, Haiyun Yuan^1,2, Shusheng Wen^1,2,*

Congenital Heart Disease, Vol.20, No.4, pp. 447-449, 2025, DOI:10.32604/chd.2025.067838 - 18 September 2025

Abstract This article has no abstract. More >

Shaocong Feng, Baojiang Cui^*, Shengjia Chang, Meiyi Jiang

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2567-2590, 2025, DOI:10.32604/cmes.2025.067326 - 31 August 2025

Abstract Service-Based Architecture (SBA) of 5G network introduces novel communication technology and advanced features, while simultaneously presenting new security requirements and challenges. Commercial 5G Core (5GC) networks are highly secure closed systems with interfaces defined through the 3rd Generation Partnership Project (3GPP) specifications to fulfill communication requirements. However, the 5GC boundary, especially the access domain, faces diverse security threats due to the availability of open-source cellular software suites and Software Defined Radio (SDR) devices. Therefore, we systematically summarize security threats targeting the N2 interfaces at the 5GC boundary, which are categorized as Illegal Registration, Protocol attack,… More >

Peng Sun^1,2, Dechen Yao^1,2,*, Jianwei Yang^1,2, Quanyu Long^1,2

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1221-1239, 2025, DOI:10.32604/sdhm.2025.066098 - 05 September 2025

Abstract Supervised learning-based rail fastener anomaly detection models are limited by the scarcity of anomaly samples and perform poorly under data imbalance conditions. However, unsupervised anomaly detection methods based on diffusion models reduce the dependence on the number of anomalous samples but suffer from too many iterations and excessive smoothing of reconstructed images. In this work, we have established a rail fastener anomaly detection framework called Diff-Fastener, the diffusion model is introduced into the fastener detection task, half of the normal samples are converted into anomaly samples online in the model training stage, and One-Step denoising… More >

Xiaoping Yang^1,2,3, Jinku Qiu^2,3,4, Xifa Gong⁵, Jin Ye⁵, Fei Yao^5,*, Jiaqiao Chen⁶, Xianzan Luo⁶, Da Qin⁶

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1515-1539, 2025, DOI:10.32604/cmc.2025.067518 - 29 August 2025

Abstract In contemporary society, rapid and accurate optical cable fault detection is of paramount importance for ensuring the stability and reliability of optical networks. The emergence of novel faults in optical networks has introduced new challenges, significantly compromising their normal operation. Machine learning has emerged as a highly promising approach. Consequently, it is imperative to develop an automated and reliable algorithm that utilizes telemetry data acquired from Optical Time-Domain Reflectometers (OTDR) to enable real-time fault detection and diagnosis in optical fibers. In this paper, we introduce a multi-scale Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) deep… More >

Abdulatif Alabdulatif^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1185-1220, 2025, DOI:10.32604/cmc.2025.066898 - 29 August 2025

Abstract FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities (IIoTCC). It introduces two key innovations: a Quantum Secure Authentication (QSA) mechanism for adversarial defense and integrity validation, and a Self-Attention Long Short-Term Memory (SALSTM) model for high-accuracy spatiotemporal anomaly detection. Addressing core challenges in traditional Federated Learning (FL)—such as model poisoning, communication overhead, and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure, real-time operation across heterogeneous IIoT domains including utilities, public safety, and traffic systems. Evaluated on the More >

Abdulwahid Al Abdulwahid^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1751-1777, 2025, DOI:10.32604/cmc.2025.066892 - 29 August 2025

Abstract The increasing sophistication of cyberattacks, coupled with the limitations of rule-based detection systems, underscores the urgent need for proactive and intelligent cybersecurity solutions. Traditional intrusion detection systems often struggle with detecting early-stage threats, particularly in dynamic environments such as IoT, SDNs, and cloud infrastructures. These systems are hindered by high false positive rates, poor adaptability to evolving threats, and reliance on large labeled datasets. To address these challenges, this paper introduces CyberGuard-X, an AI-driven framework designed to identify attack precursors—subtle indicators of malicious intent—before full-scale intrusions occur. CyberGuard-X integrates anomaly detection, time-series analysis, and multi-stage… More >

Mohammed Naif Alatawi^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 695-727, 2025, DOI:10.32604/cmc.2025.066606 - 29 August 2025

Abstract Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0. Latency, privacy risks and the complexity of industrial networks have been preventing attempts at traditional cloud-based learning systems. We demonstrate that, to overcome these challenges, for instance, the EdgeGuard-IoT framework, a 6G edge intelligence framework enhancing cybersecurity and operational resilience of the smart grid, is needed on the edge to integrate Secure Federated Learning (SFL) and Adaptive Anomaly Detection (AAD). With ultra-reliable low latency communication (URLLC) of 6G, artificial intelligence-based network orchestration, and massive machine type… More >

Chaimae Hazman^1,2, Azidine Guezzaz², Said Benkirane², Mourade Azrour^3,*, Vinayakumar Ravi⁴, Abdulatif Alabdulatif ⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 301-329, 2025, DOI:10.32604/cmc.2025.064777 - 29 August 2025

Abstract Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace. Network intrusion detection is usually regarded as an efficient means of dealing with security attacks. Many ways have been presented, utilizing various strategies and focusing on different types of visitors. Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development. Despite extensive research on anomaly-based network detection, there is still a lack of comprehensive literature reviews covering current methodologies and datasets. Despite the substantial research into anomaly-based network intrusion detection algorithms, there is a dearth of 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 >