Scalable Graph-based Bug Search for Firmware Images

By Qian Feng, Rundong Zhou, Chengcheng Xu, Yao Cheng, Brian Testa, Heng Yin. Conference on Computer and Communications Security, 2016

465 citations recorded by the source corpus.

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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.”
IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT (2016, methodology): “The vulnerability assessment can be augmented with automated penetration tests or recently developped automated bug searches techniques for IoT devices [8].”
Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations (2019, background): “[84] demonstrated how learning of high-level features of a control flow graph could improve the performance of firmware vulnerability search methods.”
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection (2017, methodology): “Our evaluation demonstrates that this task-independent model is more effective and generalize better to unseen functions than the stateof-the-art graph matching-based approach [18].”