| Title | Binary Similarity Analysis for Vulnerability Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Tai, Zeming, Washizaki, Hironori, Fukazawa, Yoshiaki, Fujimatsu, Yurie, Kanai, Jun |
| Conference Name | 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) |
| Keywords | Binary Analysis, Binary Code Search, Binary codes, binary similarity, compositionality, Computer science, Human Behavior, Measurement, Metrics, Optimization, pubcrawl, Registers, Resiliency, Software, static analysis, vulnerability detection |
| Abstract | Binary similarity has been widely used in function recognition and vulnerability detection. How to define a proper similarity is the key element in implementing a fast detection method. We proposed a scalable method to detect binary vulnerabilities based on similarity. Procedures lifted from binaries are divided into several comparable strands by data dependency, and those strands are transformed into a normalized form by our tool named VulneraBin, so that similarity can be determined between two procedures through a hash value comparison. The low computational complexity allows semantically equivalent code to be identified in binaries compiled from million lines of source code in a fast and accurate way. |
| DOI | 10.1109/COMPSAC48688.2020.0-110 |
| Citation Key | tai_binary_2020 |
Binary Similarity Analysis for Vulnerability Detection