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  • Open Access

    ARTICLE

    Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

    Tong Zu, Fengyong Li*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1395-1417, 2024, DOI:10.32604/cmes.2024.055442 - 27 September 2024

    Abstract False data injection attack (FDIA) can affect the state estimation of the power grid by tampering with the measured value of the power grid data, and then destroying the stable operation of the smart grid. Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams. Data-driven features, however, cannot effectively capture the differences between noisy data and attack samples. As a result, slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks. To address this problem, this paper designs a… More >

  • Open Access

    ARTICLE

    Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

    Fahad F. Alruwaili*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 99-115, 2023, DOI:10.32604/cmc.2023.034752 - 06 February 2023

    Abstract The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so… More >

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