Informer-Based Intrusion Detection Method for Network Attack of Integrated Energy System
Yuzhen Sun, Lu Hou, Zhengquan Lv, Daogang Peng
Abstract
This study of attack detection models for integrated energy systems is based on the Informer model. Firstly, the network data characteristics of the current integrated energy system are introduced. Secondly, the current research status of network intrusion detection is summarized. Most models ignore the time-series features of network traffic, resulting in increased detection time and memory footprint as the series grows, and the corresponding result accuracy will decrease. Informer reduces the input dimension of each layer into decoder by half by improving ProbSparse self-attention mechanism and self-distillation mechanism in encoder, thus greatly improving the above problems. And in decoder, a one-step calculation method is adopted, which significantly improves the calculation efficiency. Finally, the experiment proves that Informer’s long-sequence time model has a high accuracy in intrusion detection of integrated energy system, which verifies the effectiveness and usability of this model.