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In-vehicle network (IVN) is facing complex external cyber-attacks, especiallythe emerging masquerade attacks with extremely high difficulty of detectionwhile serious damaging effects. In this paper, we propose the STATGRAPH, whichis an effective and fine-grained intrusion detection methodology for IVNsecurity services via multi-view statistical graph learning on in-vehiclecontroller area network (CAN) messages with insight into their variations inperiodicity, payload and signal combinations. Specifically, STATGRAPH generatestwo statistical graphs, timing correlation graph (TCG) and couplingrelationship graph (CRG), in every CAN message detection window, where edgeattributes in TCGs represent temporal correlation between different message IDswhile edge attributes in CRGs denote the neighbour relationship and contextualsimilarity. Besides, a lightweight shallow layered graph convolution network istrained based on graph property of TCGs and CRGs, which learns the universallaws of various patterns more effectively and further enhance the performanceof detection. To address the problem of insufficient attack types in previousintrusion detection, we select two real in-vehicle CAN datasets covering fivenew instances of sophisticated and stealthy masquerade attacks that are neverinvestigated before. Experimental result shows STATGRAPH improves bothdetection granularity and detection performance over state-of-the-art intrusiondetection methods. Code is available athttps://github.com/wangkai-tech23/StatGraph.

STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning