The Closer You Look, The More You Learn: A Grey-box Approach to Protocol State Machine Learning
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In this paper, we propose a new approach to infer state machine models from protocol implementations. Our method, STATEINSPECTOR, learns protocol states by using novel program analyses to combine observations of run-time memory and I/O. It requires no access to source code and only lightweight execution monitoring of the implementation under test. We demonstrate and evaluate STATEINSPECTOR's effectiveness on numerous TLS and WPA/2 implementations. In the process, we show STATEINSPECTOR enables deeper state discovery, increased learning efficiency, and more insightful post-mortem analyses than existing approaches. Further to improved learning, our method led us to discover several concerning deviations from the standards and a high impact vulnerability in a prominent Wi-Fi implementation.
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