Network protocol reverse engineering is an important challenge with many security applications. A popular kind of method leverages network message traces. These methods rely on pair-wise sequence alignment and/or tokenization. They have various limitations such as difficulties of handling a large number of messages and dealing with inherent uncertainty. In this paper, we propose a novel probabilistic method for network trace based protocol reverse engineering. It first makes use of multiple sequence alignment to align all messages and then reduces the problem to identifying the keyword field from the set of aligned fields. The keyword field determines the type of a message. The identification is probabilistic, using random variables to indicate the likelihood of each field (being the true keyword). A joint distribution is constructed among the random variables and the observations of the messages. Probabilistic inference is then performed to determine the most likely keyword field, which allows messages to be properly clustered by their true types and enables the recovery of message format and state machine. Our evaluation on 10 protocols shows that our technique substantially outperforms the state-of-the-art and our case studies show the unique advantages of our technique in IoT protocol reverse engineering and malware analysis.

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