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As an important component of internet of vehicles (IoV), intelligentconnected vehicles (ICVs) have to communicate with external networksfrequently. In this case, the resource-constrained in-vehicle network (IVN) isfacing a wide variety of complex and changing external cyber-attacks,especially the masquerade attack with high difficulty of detection whileserious damaging effects that few counter measures can identify successfully.Moreover, only coarse-grained recognition can be achieved in current mainstreamintrusion detection mechanisms, i.e., whether a whole data flow observationwindow contains attack labels rather than fine-grained recognition on everysingle data item within this window. In this paper, we propose StatGraph: anEffective Multi-view Statistical Graph Learning Intrusion Detection toimplement the fine-grained intrusion detection. Specifically, StatGraphgenerates two statistical graphs, timing correlation graph (TCG) and couplingrelationship graph (CRG), based on data streams. In given message observationwindows, edge attributes in TCGs represent temporal correlation betweendifferent message IDs, while edge attributes in CRGs denote the neighbourrelationship and contextual similarity. Besides, a lightweight shallow layeredGCN network is trained based graph property of TCGs and CRGs, which can learnthe universal laws of various patterns more effectively and further enhance theperformance of detection. To address the problem of insufficient attack typesin previous intrusion detection, we select two real in-vehicle CAN datasetsthat cover four new attacks never investigated before. Experimental resultshows StatGraph improves both detection granularity and detection performanceover state-of-the-art intrusion detection methods.

Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning on CAN Messages