BinHunt: Automatically Finding Semantic Differences in Binary Programs

By Debin Gao, Michael K. Reiter, Dawn Song. International Conference on Information, Communications and Signal Processing, 2008

298 citations recorded by the source corpus.

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Scalable Graph-based Bug Search for Firmware Images (2016, methodology): “Besides, BinHunt [26] and iBinHunt [39] utilize symbolic execution and a theorem prover to check semantic equivalence be-tween basic blocks.”
Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization (2019, background): “BinHunt [36], iBinHunt [37], and ESH [18] use a theorem prover to verify whether two basic blocks or strands are equivalent.”
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection (2017, methodology): “The symbolic execution and theorem prover used in BinHunt[20] and iBinHunt [29] are by design expensive, since they need to extract the equations and conduct the equivalent checking.”
Classification of malware using structured control flow (2010, methodology): “BinHunt (Gao et al. 2008) provides a more thorough test of flowgraph similarity by soundly identifying the maximum common subgraph, but at reduced levels of performance and without application to malware classification.”
Juxtapp: A Scalable System for Detecting Code Reuse among Android Applications (2012, methodology): “Instead of using boolean features, Gao et al.[22] and Hu et al.”
Cross-architecture bug search in binary executables (2015, methodology): “A follow-up project, IBINHUNT [29], augments BinHunt with taint analysis to reduce the number of possible matches.”