Research on Anomaly Detection of Train Communication Network Based on Long and Short-term Memory Network

Abstract

Aiming at the intrusion detection algorithm based on traditional machine learning, which can not effectively deal with the problems of large quantity, strong timing and high dimension in the train communication network system, this paper proposes an intrusion detection algorithm based on adaptive momentum estimation optimization of long and short-term memory networks. This article first introduces the principles and characteristics of the long and short-term memory network, and then preprocesses the train communication network information according to the characteristics of the train communication network mode, proposes a train communication network anomaly detection model based on the long and short-term memory network, and studies different parameters The impact on the performance of anomaly detection, and the comparison of multiple intrusion detection performance indicators with other traditional shallow machine learning methods, proves the effectiveness of the algorithm proposed in this paper.