A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems. (arXiv:2210.06729v1 [cs.LG])

This paper presents a novel data-driven framework to aid in system state
estimation when the power system is under unobservable false data injection
attacks. The proposed framework dynamically detects and classifies false data
injection attacks. Then, it retrieves the control signal using the acquired
information. This process is accomplished in three main modules, with novel
designs, for detection, classification, and control signal retrieval. The
detection module monitors historical changes in phasor measurements and
captures any deviation pattern caused by an …

attacks cyber data identification injection injection attacks physical power power systems stream systems