Derrick McKee (Purdue University), Nathan Burow (MIT Lincoln Laboratory), Mathias Payer (EPFL)

Reverse engineering unknown binaries is a difficult, resource intensive process due to information loss and optimizations performed by compilers that introduce significant binary diversity. Existing binary similarity approaches do not scale or are inaccurate. In this paper, we introduce IOVec Function Identification (IOVFI), which assesses similarity based on program state transformations, which compilers largely guarantee even across compilation environments and architectures. IOVFI executes functions with initial predetermined program states, measures the resulting program state changes, and uses the sets of input and output state vectors as unique semantic fingerprints. Since IOVFI relies on state vectors, and not code measurements, it withstands broad changes in compilers and optimizations used to generate a binary.

Evaluating our IOVFI implementation as a semantic function identifier for coreutils-8.32, we achieve a high .773 average F-Score, indicating high precision and recall. When identifying functions generated from differing compilation environments, IOVFI achieves a 100% accuracy improvement over BinDiff 6, outperforms asm2vec in cross-compilation environment accuracy, and, when compared to dynamic frameworks, BLEX and IMF-SIM, IOVFI is 25%–53% more accurate.

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Faysal Hossain Shezan (University of Virginia), Zihao Su (University of Virginia), Mingqing Kang (Johns Hopkins University), Nicholas Phair (University of...

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project...

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