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.
Highlights
Accepted: 27 January 2022In recent years, the traditional mechanical interface to the vehicle system has been replaced by communication between electronic control units (ECUs) or minicomputers that coordinate subsystems [1]
In order to overcome the above problems, we propose TCAN-intrusion detection systems (IDSs), an intrusion detection method on controller area network based on the temporal convolutional network (TCN) [27]
ER is an important metric for intrusion detection systems, and a low error rate ensures that users do not receive frequent false alarms
Summary
The traditional mechanical interface to the vehicle system has been replaced by communication between electronic control units (ECUs) or minicomputers that coordinate subsystems [1]. Each ECU is the implementer of a specific function in the control system, such as throttle, brake, and steering. The ECUs are segmented into different subnets according to their function or communication rate. The control area network (CAN) of in-vehicle interconnects the subnets via multiple gateways, providing an efficient, reliable, and economical communication channel. The vehicle ecosystem is moving towards being internet-connected, intelligent, and modernized [2]. The Internet of Vehicles (IoV) has enabled the world to witness a deluge of data that is building a new product of the Internet of Everything
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