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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 11

Vulnerability classification method based on double-attention mechanism and adversarial training

Yang Jinneng1
Li Wenshan1,2
He Junjiang1
Zhou Shaohong1
Li Tao1
Wang Yunpeng1,3
1. School of Cyber Science & Engineering, Sichuan University, Chengdu Sichuan 610207, China
2. School of Cyber Science & Engineering, Chengdu University of Information Technology, Chengdu Sichuan 610225, China
3. Smart Rongcheng Operation Center, Xindu District, Chengdu Sichuan 610095, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0061

Abstract

Vulnerability reports play a pivotal role in cybersecurity, and the ever-growing number of vulnerabilities challenges the efficiency and accuracy of vulnerability classification. To alleviate issues with deep learning models in vulnerability classification, which often fail to focus on significant features and are prone to overfitting, this study introduced a novel vulnerability classification approach based on a double attention mechanism and improved adversarial training. Firstly, this paper proposed the TextCNN-DA model, which augments the conventional TextCNN with spatial and channel attention mechanisms to enhance focus on pertinent features. Further, this paper introduced the SWV-FGM algorithm for adversarial training to increase the robustness and generalization of the model. Comparative analysis with other baseline algorithms on a vulnerability dataset, and specific performance evaluation across different vulnerability types, show that our method outperforms in several key metrics such as Accuracy and Macro-F1, effectively advancing vulnerability classification tasks.

Foundation Support

国家重点研发计划资助项目(2020YFB1805400)
国家自然科学基金资助项目(62032002,62101358)
四川省科技计划重点研发项目(2023YFG0294)
四川省自然科学青年基金资助项目(2023NSFSC1395)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0061
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 11

Publish History

[2024-07-10] Accepted Paper

Cite This Article

杨尽能, 李汶珊, 何俊江, 等. 基于双注意力机制和改进对抗训练的漏洞分类方法 [J]. 计算机应用研究, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0061. (Yang Jinneng, Li Wenshan, He Junjiang, et al. Vulnerability classification method based on double-attention mechanism and adversarial training [J]. Application Research of Computers, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0061. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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