@inproceedings{00a43326df6a49cf9b3612f497c77515,
title = "Binary Similarity Analysis for 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.",
keywords = "binary analysis, binary code search, binary similarity, static analysis",
author = "Zeming Tai and Hironori Washizaki and Yoshiaki Fukazawa and Yurie Fujimatsu and Jun Kanai",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 ; Conference date: 13-07-2020 Through 17-07-2020",
year = "2020",
month = jul,
doi = "10.1109/COMPSAC48688.2020.0-110",
language = "English",
series = "Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1121--1122",
editor = "Chan, {W. K.} and Bill Claycomb and Hiroki Takakura and Ji-Jiang Yang and Yuuichi Teranishi and Dave Towey and Sergio Segura and Hossain Shahriar and Sorel Reisman and Ahamed, {Sheikh Iqbal}",
booktitle = "Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020",
}