A Cross-Layer Defense Scheme for Edge Intelligence-Enabled CBTC Systems Against MitM Attacks

Abstract

While communication-based train control (CBTC) systems play a crucial role in the efficient and reliable operation of urban rail transits, its high penetration level of communication networks opens doors to Man-in-the-Middle (MitM) attacks. Current researches regarding MitM attacks do not consider the characteristics of CBTC systems. Particularly, the limited computing capability of the on-board computers prevents the direct implementation of most existing intrusion detection and defense algorithms against the MitM attack. In order to tackle this dilemma, in this article, we first introduce edge intelligence (EI) into CBTC systems to enhance the computing capability of the system. A cross-layer defense scheme, which includes the detection and defense stages, are proposed next. For the cross-layer detection stage, we propose a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) based detection method to combine the detection probability calculated from the train control parameter sequence and operation log files. For the cross-layer defense stage, we construct a Bayesian game based defense model to derive the optimal defense policy against MitM attacks. To further improve the accuracy of the defense scheme as well as optimize the communication resource allocation scheme, we propose an optimal communication resource allocation scheme based on the Asynchronous Advantage Actor-Critic (A3C) algorithm at last. Extensive simulation results show that the proposed scheme achieves excellent performance in defending against MitM attacks.