A Review of Data Representation Methods for Vulnerability Mining Using Deep Learning

Ying Li, Mianxue Gu, Hongyu Sun, Yuhao Lin, Qiuling Yue, Zhen Guo, Jinglu Hu, He Wang, Yuqing Zhang*

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

The rapid development of software has brought unprecedented severe challenges to software security vulnerabilities. Traditional vulnerability mining methods are difficult to apply to large-scale software systems due to drawbacks such as manual inspection, low efficiency, high false positives and high false negatives. Recent research works have attempted to apply deep learning models to vulnerability mining, and have made a good progress in vulnerability mining filed. In this paper, we analyze the deep learning model framework applied to vulnerability mining and summarize its overall workflow and technology. Then, we give a detailed analysis on five feature extraction methods for vulnerability mining, including sequence characterization-based method, abstract syntax tree-based method, graph-based method, text-based method and mixed characterization-based method. In addition, we summarize their advantages and disadvantages from the angles of single and mixed feature extraction method. Finally, we point out the future research trends and prospects.

本文言語English
ホスト出版物のタイトルFrontiers in Cyber Security - 4th International Conference, FCS 2021, Revised Selected Papers
編集者Chunjie Cao, Yuqing Zhang, Yuan Hong, Ding Wang
出版社Springer Science and Business Media Deutschland GmbH
ページ342-351
ページ数10
ISBN(印刷版)9789811905223
DOI
出版ステータスPublished - 2022
イベント4th International Conference on Frontiers in Cyber Security, FCS 2021 - Haikou, China
継続期間: 2021 12月 172021 12月 19

出版物シリーズ

名前Communications in Computer and Information Science
1558 CCIS
ISSN(印刷版)1865-0929
ISSN(電子版)1865-0937

Conference

Conference4th International Conference on Frontiers in Cyber Security, FCS 2021
国/地域China
CityHaikou
Period21/12/1721/12/19

ASJC Scopus subject areas

  • コンピュータサイエンス一般
  • 数学一般

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