A novel machine learning based intrusion detection method for 5G empowered CBTC systems

Wireless communication is the basis for ensuring the safe and efficient operation of rail transit trains. The advantages of 5G technology such as high reliability, high speed and low latency make it a very broad application prospect in the field of rail transit. However, 5G networks also face corresponding security threats in application, such as various network attacks. The communication-based train control (CBTC) systems are vulnerable to network attacks, which can easily lead to emergency braking or collision of trains when it was attacked. In order to protect network security, the intrusion detection systems (IDS) can be used. The IDS currently applied to the CBTC systems have problems such as low detection rate and high false rate. Therefore, this paper proposes an intrusion detection system based on machine learning, and simulates the real network environment by using KDD99 data. First, the data is preprocessed and feature extracted. Then two methods of decision tree and neural network are used to train the classifiers. In addition, the multiple classifiers are trained on the basis of the binary classifiers. Finally, we test the performance of the classifiers. The results show that the classifiers have a good detection accuracy of network traffic.