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When reverse engineering a binary, the analyst must first understand thesemantics of the binary's functions through either manual or automaticanalysis. Manual semantic analysis is time-consuming, because abstractionsprovided by high level languages, such as type information, variable scope, orcomments are lost, and past analyses cannot apply to the current analysis task.Existing automated binary analysis tools currently suffer from low accuracy indetermining semantic function identification in the presence of diversecompilation environments.  We introduce Software Ethology, a binary analysis approach for determiningthe semantic similarity of functions. Software Ethology abstracts semanticbehavior as classification vectors of program state changes resulting from afunction executing with a specified input state, and uses these vectors as aunique fingerprint for identification. All existing semantic identifiersdetermine function similarity via code measurements, and suffer from highinaccuracy when classifying functions from compilation environments differentfrom their ground truth source. Since Software Ethology does not rely on codemeasurements, its accuracy is resilient to changes in compiler, compilerversion, optimization level, or even different source implementing equivalentfunctionality.  Tinbergen, our prototype Software Ethology implementation, leverages avirtual execution environment and a fuzzer to generate the classificationvectors. In evaluating Tinbergen's feasibility as a semantic functionidentifier by identifying functions in coreutils-8.30, we achieve a high .805average accuracy. Compared to the state-of-the-art, Tinbergen is 1.5 orders ofmagnitude faster when training, 50% faster in answering queries, and, whenidentifying functions in binaries generated from differing compilationenvironments, is 30%-61% more accurate.

Software Ethology: An Accurate, Resilient, and Cross-Architecture Binary Analysis Framework