TCAN-IDS: Intrusion Detection System for Internet of Vehicle Using Temporal Convolutional Attention Network

Abstract

Intrusion detection systems based on recurrent neural network (RNN) have been considered as one of the effective methods to detect time-series data of in-vehicle networks. However, building a model for each arbitration bit is not only complex in structure but also has high computational overhead. Convolutional neural network (CNN) has always performed excellently in processing images, but they have recently shown great performance in learning features of normal and attack traffic by constructing message matrices in such a manner as to achieve real-time monitoring but suffer from the problem of temporal relationships in context and inadequate feature representation in key regions. Therefore, this paper proposes a temporal convolutional network with global attention to construct an in-vehicle network intrusion detection model, called TCAN-IDS. Specifically, the TCAN-IDS model continuously encodes 19-bit features consisting of an arbitration bit and data field of the original message into a message matrix, which is symmetric to messages recalling a historical moment. Thereafter, the feature extraction model extracts its spatial-temporal detail features. Notably, global attention enables global critical region attention based on channel and spatial feature coefficients, thus ignoring unimportant byte changes. Finally, anomalous traffic is monitored by a two-class classification component. Experiments show that TCAN-IDS demonstrates high detection performance on publicly known attack datasets and is able to accomplish real-time monitoring. In particular, it is anticipated to provide a high level of symmetry between information security and illegal intrusion.