LAUSR

False data injection (FDI) attacks, as a new class of cyberattacks, bring a severe threat to the security and reliable operation of the smart grid by damaging the state estimation of the power system. To address this issue, an extreme learning machine (ELM)-based one-class-one-network (OCON) framework is proposed for detecting the FDI attacks in this paper. Under this framework, to effectively detect bus-based FDI attacks and identify the bus node being attacked, the subnets of state identification layer in OCON adopt the ELM algorithm to accurately divide the false data and the normal data. After that, a global layer is employed to analyze whether the bus node associated with its corresponding subnet is attacked by false data utilizing the results from the state identification layer. Finally, in order to improve the resilience of the power system, a prediction recovery strategy is proposed to remedy the detected false data by exploiting the spatial correlation of power data. The proposed framework is tested on the IEEE 14 bus system using real load data from New York independent system operator. The simulation results demonstrate that the proposed framework not only accurately recognizes the multiple bus nodes under the FDI attacks but also efficiently recovers the data injected by false data.