PROGRAPHER: An Anomaly Detection System based on Provenance Graph Embedding

In recent years, the Advanced Persistent Threat (APT), which involves complex and malicious actions over a long period, has become one of the biggest threats against the security of the modern computing environment. As a countermeasure, data provenance is leveraged to capture the complex relations between entities in a computing system/network, and uses such information to detect sophisticated APT attacks. Though showing promise in countering APT attacks, the existing systems still cannot achieve a good balance between efficiency, accuracy, and granularity. In this work, we design a new anomaly detection system on provenance graphs, termed PROGRAPHER. To address the problem of "dependency explosion" of provenance graphs and achieve high efficiency, PROGRAPHER extracts temporalordered snapshots from the ingested logs and performs detection on the snapshots. To capture the rich structural properties of a graph, whole graph embedding and sequence-based learning are applied. Finally, key indicators are extracted from the abnormal snapshots and reported to the analysts, so their workload will be greatly reduced. We evaluate PROGRAPHER on five real-world datasets. The results show that PROGRAPHER can detect standard attacks and APT attacks with high accuracy and outperform the stateof-the-art detection systems.