Hypercube Graph Self-Attention Mechanisms for Intelligent Vehicular Intrusion Detection in Autonomous Transport Systems

Autonomous Transportation Systems (ATS) make the transportation system transition from “passive transportation” to “autonomous service”. The wireless nature of communication in ATS presents significant cybersecurity challenges. Conventional intelligent vehicular intrusion detection methods may not suffice in situations where vehicular data is produced at an unprecedented scale and diverse cybersecurity threats are launched. Therefore, there is a demand for the creation of advanced intelligent vehicular intrusion detection systems that can effectively manage potential cyberattacks within ATS. Toward this end, this paper proposes QnGSA (hypercube driven graph self-attention intelligent vehicular intrusion detection model) in ATS, a novel intrusion detection model that helps to protect both the vehicles and the data they transmit, preventing disruptions to services, theft of sensitive information, and potential harm to passengers or cargo. QnGSA not only proposes a construction method of association graph by introducing hypercube and semi-supervised K-means++ clustering algorithm (QnSSKM). But also, QnGSA self-extracts the graph structural information of hypercube, and uses the graph self-attention mechanism to aggregate node features and obtain more accurate representation. Furthermore, this paper uses Graph Attention Network classifier to correlate the learned node representation with the fault category, and uses Softmax function to map the node representation to the probability distribution of different categories. The category with the highest probability is selected as the prediction label of the node, so as to realize the intelligent vehicular intrusion detection. Experiments results show that our proposed QnGSA method achieves the best results compared with state-of-the-art methods in terms of accuracy, macro precision/recall/F1.