Flash: A Comprehensive Approach to Intrusion Detection via Provenance Graph Representation Learning

Recently, provenance-based Intrusion Detection Systems (IDSes) have gained popularity for their potential in detecting sophisticated Advanced Persistent Threat (APT) attacks. These IDSes employ provenance graphs created from system logs to identify potentially malicious activities. Despite their potential, they face challenges in accuracy, practicality, and scalability, particularly when dealing with large provenance graphs. We present Flash, a scalable IDS that leverages graph representation learning through Graph Neural Networks (GNNs) on data provenance graphs to overcome these limitations. Flash employs a Word2Vec-based semantic encoder to capture essential semantic attributes (e.g., process names and file paths) and the temporal ordering of events within the provenance graph. Furthermore, Flash incorporates a novel adaptation of a GNN-based contextual encoder to efficiently encode both local and global graph structures into expressive node embeddings. To learn benign node behaviors, we utilize a lightweight classifier that combines the GNN and Word2Vec embeddings. Recognizing the computational demands and slow processing times of GNN, particularly for large provenance graphs, we have developed an embedding recycling database to store the node embeddings generated during the training phase. During runtime, our lightweight classifier leverages the stored embeddings, obviating the need to regenerate GNN embeddings, thus facilitating real-time APT detection. Extensive evaluation of Flash on real-world datasets demonstrates superior detection accuracy compared to existing provenance-based IDSes. The results also illustrate Flash’s scalability, robustness against mimicry attacks, and potential for accelerating the alert verification process.