Mining Software Component Interactions to Detect Security Threats at the Architectural Level

Conventional security mechanisms at network, host, and source code levels are no longer sufficient in detecting and responding to increasingly dynamic and sophisticated cyber threats today. Detecting anomalous behavior at the architectural level can help better explain the intent of the threat and strengthen overall system security posture. To that end, we present a framework that mines software component interactions from system execution history and applies a detection algorithm to identify anomalous behavior. The framework uses unsupervised learning at runtime, can perform fast anomaly detection on the fly, and can quickly adapt to system load fluctuations and user behavior shifts. Our evaluation of the approach against a real Emergency Deployment System has demonstrated very promising results, showing the framework can effectively detect covert attacks, including insider threats, that may be easily missed by traditional intrusion detection methods.