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.

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The popularity of Internet of Vehicles (IoV) has made people's driving environment more comfortable and convenient. However, with the integration of external networks and the vehicle networks, the vulnerabilities of the Controller Area Network (CAN) are exposed, allowing attackers to remotely invade vehicle networks through external devices. Based on the remote attack model for vulnerabilities of the in-vehicle CAN, we designed an efficient and safe identity authentication scheme based on Feige-Fiat-Shamir (FFS) zero-knowledge identification scheme with extremely high soundness. We used the method of zero-one reversal and two-to-one verification to solve the problem that FFS cannot effectively resist guessing attacks. Then, we carried out a theoretical analysis of the scheme's security and evaluated it on the software and hardware platform. Finally, regarding time overhead, under the same parameters, compared with the existing scheme, the scheme can complete the authentication within 6.1ms without having to go through multiple rounds of interaction, which reduces the additional authentication delay and enables all private keys to participate in one round of authentication, thereby eliminating the possibility that a private key may not be involved in the original protocol. Regarding security and soundness, as long as private keys are not cracked, the scheme can resist guessing attacks, which is more secure than the existing scheme.

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Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertainty and diversity regarding attack methods make this task challenging. However, the existing works have the limitation of only considering local features or the weak feature mapping of multifeatures. To address these limitations, we present a novel model for automotive intrusion detection by spatial-temporal correlation (STC) features of in-vehicle communication traffic (intrusion detection system [IDS]). Specifically, the proposed model exploits an encodingdetection architecture. In the encoder part, spatial and temporal relations are encoded simultaneously. To strengthen the relationship between features, the attention-based convolutional network still captures spatial and channel features to increase the receptive field, while attention-long short-term memory builds meaningful relationships from previous time series or crucial bytes. The encoded information is then passed to detector for generating forceful spatial-temporal attention features and enabling anomaly classification.In particular, single-frame and multiframe models are constructed to present different advantages, respectively. Under automatic hyperparameter selection based on