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Binary similarity detection is a critical technique that has been applied inmany real-world scenarios where source code is not available, e.g., bug search,malware analysis, and code plagiarism detection. Existing works are ineffectivein detecting similar binaries in cases where different compiling optimizations,compilers, source code versions, or obfuscation are deployed.  We observe that all the cases do not change a binary's key code behaviorsalthough they significantly modify its syntax and structure. With this keyobservation, we extract a set of key instructions from a binary to capture itskey code behaviors. By detecting the similarity between two binaries' keyinstructions, we can address well the ineffectiveness limitation of existingworks. Specifically, we translate each extracted key instruction into aself-defined key expression, generating a key-semantics graph based on thebinary's control flow. Each node in the key-semantics graph denotes a keyinstruction, and the node attribute is the key expression. To quantify thesimilarity between two given key-semantics graphs, we first serialize eachgraph into a sequence of key expressions by topological sort. Then, we tokenizeand concatenate key expressions to generate token lists. We calculate thelocality-sensitive hash value for all token lists and quantify theirsimilarity. %We implement a prototype, called SemDiff, consisting of twomodules: graph generation and graph diffing. The first module generates a pairof key-semantics graphs and the second module diffs the graphs. Our evaluationresults show that overall, SemDiff outperforms state-of-the-art tools whendetecting the similarity of binaries generated from different optimizationlevels, compilers, and obfuscations. SemDiff is also effective for libraryversion search and finding similar vulnerabilities in firmware.

SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs