Binary Similarity Analysis for Vulnerability Detection

Zeming Tai, Hironori Washizaki, Yoshiaki Fukazawa, Yurie Fujimatsu, Jun Kanai

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
編集者W. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1121-1122
ページ数2
ISBN(電子版)9781728173030
DOI
出版ステータスPublished - 2020 7
イベント44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 - Virtual, Madrid, Spain
継続期間: 2020 7 132020 7 17

出版物シリーズ

名前Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020

Conference

Conference44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020
国/地域Spain
CityVirtual, Madrid
Period20/7/1320/7/17

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • ソフトウェア
  • 教育

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