Probabilistic Program Modeling for High-Precision Anomaly Classification

The trend constantly being observed in the evolution of advanced modern exploits is their growing sophistication in stealthy attacks. Code-reuse attacks such as return-oriented programming allow intruders to execute mal-intended instruction sequences on a victim machine without injecting external code. We introduce a new anomaly-based detection technique that probabilistically models and learns a program's control flows for high-precision behavioral reasoning and monitoring. Our prototype in Linux is named STILO, which stands for STatically InitiaLized markOv. Experimental evaluation involves real-world code-reuse exploits and over 4,000 testcases from server and utility programs. STILO achieves up to 28-fold of improvement in detection accuracy over the state-of-the-art HMM-based anomaly detection. Our findings suggest that the probabilistic modeling of program dependences provides a significant source of behavior information for building high-precision models for real-time system monitoring.