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A key element in defending computer networks is to recognize the types ofcyber attacks based on the observed malicious activities. Obfuscation onto whatcould have been observed of an attack sequence may lead to mis-interpretationof its effect and intent, leading to ineffective defense or recoverydeployments. This work develops probabilistic graphical models to generalize afew obfuscation techniques and to enable analyses of the ExpectedClassification Accuracy (ECA) as a result of these different obfuscation onvarious attack models. Determining the ECA is a NP-Hard problem due to thecombinatorial number of possibilities. This paper presents severalpolynomial-time algorithms to find the theoretically bounded approximation ofECA under different attack obfuscation models. Comprehensive simulation showsthe impact on ECA due to alteration, insertion and removal of attack actionsequence, with increasing observation length, level of obfuscation and modelcomplexity.

Probabilistic Modeling and Inference for Obfuscated Cyber Attack Sequences