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    ARTICLE

    A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

    Weijian Song1,, Xi Li1,, Peng Chen1,*, Juan Chen1, Jianhua Ren2, Yunni Xia3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563

    Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >

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