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Tackling binary program analysis problems has traditionally implied manuallydefining rules and heuristics, a tedious and time-consuming task for humananalysts. In order to improve automation and scalability, we propose analternative direction based on distributed representations of binary programswith applicability to a number of downstream tasks. We introduce Bin2vec, a newapproach leveraging Graph Convolutional Networks (GCN) along with computationalprogram graphs in order to learn a high dimensional representation of binaryexecutable programs. We demonstrate the versatility of this approach by usingour representations to solve two semantically different binary analysis tasks -functional algorithm classification and vulnerability discovery. We compare theproposed approach to our own strong baseline as well as published results anddemonstrate improvement over state-of-the-art methods for both tasks. Weevaluated Bin2vec on 49191 binaries for the functional algorithm classificationtask, and on 30 different CWE-IDs including at least 100 CVE entries each forthe vulnerability discovery task. We set a new state-of-the-art result byreducing the classification error by 40% compared to the source-code-basedinst2vec approach, while working on binary code. For almost every vulnerabilityclass in our dataset, our prediction accuracy is over 80% (and over 90% inmultiple classes).

Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks