Deep self-coding information attack detection and power privacy protection research based on power system state
Electronic information and communication technologies have driven the renewal of power systems.Information technol-ogy has brought development to power systems while the potential information security problems have become progressively more se-vere.The study proposes a method for detecting quantitative tampering attacks based on a deep auto-encoder and a framework for es-timating privacy protection based on homomorphic cryptography.The simulation results show that the accuracy rate of the algorithm gradually stabilises at around 90％as the threshold increases,while the accuracy rate,recall rate and F1 value show a decreasing trend,with the maximum F1 value being 91.07,corresponding to the optimal threshold of 8.The accuracy rate of the algorithm is 0.927,the accuracy rate is 0.934,the recall rate is 0.847 and the F1 value is 0.912,with all four performance items performing well and improving significantly.The mean normal measurement error for the IEEE9 node system was 2.367 and the abnormal meas-urement error was 22.781,with significant differences clearly distinguishing the reconfiguration error of the abnormal measurements.When the key is 1536,the operating efficiency is still high and the root mean square error size meets the actual engineering needs,ef-fectively guaranteeing the safe operation of the power system.
power systemsdeep learningattackspower privacycryptography
万方数据
维普
