A Sharper Sense of Self: Probabilistic Reasoning of Program Behaviors for Anomaly Detection with Context Sensitivity

Abstract

Program anomaly detection models legitimate behaviors of complex software and detects deviations during execution. Behavior deviations may be caused by malicious exploits, design flaws, or operational errors. Probabilistic detection computes the likelihood of occurrences of observed call sequences. However, maintaining context sensitivity in detection incurs high modeling complexity and runtime overhead. We present a new anomaly-based detection technique that is both probabilistic and 1-level calling-context sensitive. We describe a matrix representation and clustering-based solution for model reduction, specifically reducing the number of hidden states in a special hidden Markov model whose parameters are initialized with program analysis. Our extensive experimental evaluation confirms the significantly improved detection accuracy and shows that attacker's ability to conduct code-reuse exploits is substantially limited.