Deepro: Provenance-based APT Campaigns Detection via GNN.


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Advanced Persistent Threats (APTs) are typically sophisticated, stealthy and long-term attacks that are difficult to be detected and investigated. Recently proposed provenance graph based on system audit logs has become an important approach for APT detection and investigation. However, existing provenance-based approaches that either require rules based on expert knowledge or cannot pinpoint attack events in a provenance graph still cannot effectively mitigate APT attacks. In this paper, we present DEEPRO, a provenance-based APT campaign detection approach that not only effectively detects attack-relevant entities in a provenance graph but also precisely recovers APT campaigns based on the detected entities. Specifically, DEEPRO first customizes a general purpose GNN (Graph Neural Network) model to represent and detect process nodes in a provenance graph through automatically learning different patterns of attack behaviors and benign behaviors using the rich contextual information in the provenance graph. Then, DEEPRO further detects attack-relevant file and network entities according to their data dependencies with the detected process nodes. Finally, DEEPRO recovers APT campaigns through correlating detected entities based on their causality relationships in the provenance graph. We evaluated DEEPRO with ten real-world APT attacks. The evaluation result shows that DEEPRO can effectively detect attack events with an average 98.81% F-1-score and thus produces precise provenance sub-graphs of APT attacks.
attack detection, data provenance, graph neural networks
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