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Abnormal messages propagated from faulty operations in an in-vehicle network may severely harm a vehicular system, but they cannot be easily detected when their information is not known in advance. To support an efficient detection of faulty message patterns propagated in the in-vehicle network, this paper presents a novel graph pattern matching framework built upon a message log-driven graph modeling. Our framework models the unknown operation of the in-vehicle network as a query graph and the reference database of normal operations as data graphs. Given a query graph and data graphs, we determine whether the query graph represents normal or fault operation by using the distance measure on the data graphs. The analysis of the faulty message propagation requires to consider the sequence of events in the distance measure, and thus, using the conventional graph distance measures can …