In recent years, the secure operation of power systems has grasped the focus of researchers' attention, and fuzzing test-based vulnerability mining has become a commonly used tool for evaluating its stability and robustness in the face of various anomalies. In fuzzing testing, test case generation is concerned with the ultimate performance of vulnerability mining, however, existing techniques generally focus only on coverage and not on the vulnerable parts. Therefore, based LSTM arrays that combined with residual connections, we propose a novel approach to learn the vulnerable parts of the power system to guide the process of fuzzing by utilizing the powerful feature representation capability of neural networks. Experiments show that the improved method can achieve faster convergence and higher vulnerability detection rate of neural networks, while the FNR of which is 20.4% and TPR is 79.6%.
The electric power system is evolving into an electric cyber-physical system (ECPS) that is highly integrated with cyberspace, realizing real-time interaction and deep integration of information flow and energy flow. The obvious security risk of ECPS is that the security risks in cyberspace and physical space will superimpose each other and form a chain failure across space. In the current security technology of ECPS, the detection methods for information threats and power system faults lack internal correlation and are isolated from each other, so it is impossible to show the overall security status of ECPS from a macro level. This paper proposes a threat situation assessment framework for ECPS, and conducts a macro analysis of the overall security status of ECPS, filling in the current lack of technical gaps in the detection of cross-space cascading faults caused by information threats.
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