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  • Open Access

    ARTICLE

    Deep Feature-Driven Hybrid Temporal Learning and Instance-Based Classification for DDoS Detection in Industrial Control Networks

    Haohui Su1, Xuan Zhang1,*, 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 >

  • Open Access

    ARTICLE

    Online Rail Fastener Detection Based on YOLO Network

    Jun Li1, Xinyi Qiu1, Yifei Wei1,*, Mei Song1, Xiaojun Wang2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5955-5967, 2022, DOI:10.32604/cmc.2022.027947 - 21 April 2022

    Abstract Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production. Track is the basic equipment of railway transportation, and its performance directly affects the service lifetime of railway lines and vehicles. The anomaly detection of rail fasteners is in a priority, while the traditional manual method is extremely inefficient and dangerous to workers. Therefore, this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners, but also to recognize the fasteners states. To be more specific, this paper mainly studies… More >

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