Probabilistic inference for obfuscated network attack sequences

Facing diverse network attack strategies and overwhelming alters, much work has been devoted to correlate observed malicious events to pre-defined scenarios, attempting to deduce the attack plans based on expert models of how network attacks may transpire. Sophisticated attackers can, however, employ a number of obfuscation techniques to confuse the alert correlation engine or classifier. Recognizing the need for a systematic analysis of the impact of attack obfuscation, this paper models attack strategies as general finite order Markov models, and treats obfuscated observations as noises. Taking into account that only finite observation window and limited computational time can be afforded, this work develops an algorithm to efficiently inference on the joint distribution of clean and obfuscated attack sequences. The inference algorithm recovers the optimal match of obfuscated sequences to attack models, and enables a systematic and quantitative analysis on the impact of obfuscation on attack classification.