A Rule Mining-Based Advanced Persistent Threats Detection System
This work addresses early detection of stealthy cyber-attacks for organizations, representing an incremental improvement in domain-specific security.
The paper tackles the problem of detecting advanced persistent threats (APTs) by introducing an unsupervised method that uses OS-independent features from provenance traces to rank anomalous processes based on frequent and rare event associations, achieving superior performance on DARPA datasets compared to competing approaches.
Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time. Blocking all APTs is impossible, security experts caution, hence the importance of research on early detection and damage limitation. Whole-system provenance-tracking and provenance trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur. We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces. Anomalous processes are ranked using both frequent and rare event associations learned from traces. Results are then presented as implications which, since interpretable, help leverage causality in explaining the detected anomalies. When evaluated on Transparent Computing program datasets (DARPA), our method outperformed competing approaches.