Network Behavior Abnormal Detection for Electricity Management System Based on Long Short-Term Memory
Jiye Wang, Bingfeng Cui · 2018
In this paper, we propose a deep learning based algorithm to implement the network behavior abnormal detection for electricity management system. The framework is composed by three main parts: network behavior feature generation, behavior labeling system, and LSTM based audition model. In the feature generation module, the user log file can be converted into time series of vectors that represent the features of user operation. Then the user behavior is labeled as normal or abnormal by exporters or defined simple rules. Finally, a LSTM based deep learning model will be trained from the tagged user log features and will be used to automatically audit the user behavior. According to the experimental results on simulated user log, the proposed algorithm can effectively detect the multi-type abnormal behaviors defined by exporters in the Electricity management system. The overall accuracy in the test dataset is over 96% which can improve the existing system in large extent.