discovRE: Efficient Cross-Architecture Identification of Bugs in Binary Code

By Sebastian Eschweiler, Khaled Yakdan, Elmar Gerhards-Padilla. Network and Distributed System Security Symposium, 2016

352 citations recorded by the source corpus.

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Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection (2017, methodology): “There have been several previous works addressing the bug-search problem: discovRE [16], Multi-HM andMulti-k-HM [31], a centroid-based search [10], and Genius [18].”
Graph Matching Networks for Learning the Similarity of Graph Structured Objects (2019, methodology): “In the past the binary function similarity search problem has been tackled with classical graph theoretical matching algorithms (Eschweiler et al., 2016; Pewny et al., 2015), and Xu et al. (2017) and Feng et al.”
Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization (2019, methodology): “ILine [27], Discovre [7], Genius [6], BinSign [30], and BinShape [31] construct descriptive statistic features, such as ratio of arithmetic assembly instructions, ratio of transfer instructions, number of basic blocks, and number of function calls, among others.”