Provenance graph-oriented host intrusion detection method and system, and storage medium

Abstract

The present invention discloses a provenance graph-oriented host intrusion detection method and system, and a storage medium, which relates to the field of cyber security. The method includes: S, acquiring provenance data of a host to be tested, to construct a provenance graph representing user behaviors; S, mapping nodes in the provenance graph to roles, constructing a node feature matrix composed of feature vectors which can be used to represent attribute features, structural features, and inter-node interactive relationship of the nodes in the provenance graph, and mapping nodes having similar feature vectors to the same role; S, performing an attention-guided attribute temporal random walk by comprehensively considering attributes of the nodes in the provenance graph, the temporal relationship between edges, and an attention parameter between different roles; and S, converting the acquired attribute temporal random walk sequence into an embedding vector to extract a feature of the provenance graph, and performing intrusion anomaly detection. The present invention can perform deep representation learning on provenance data, reduce the workload of training a detection model, and improve the accuracy and efficiency of intrusion detection.