Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)

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Research on Deep Learning Vulnerability Detection Method Based on Fusion Features

Authors
Shuai Liu1, *, Guan Wang1
1School of Information Security, University of Technology of Beijing, Beijing, China
*Corresponding author. Email: netomark@126.com
Corresponding Author
Shuai Liu
Available Online 26 September 2023.
DOI
10.2991/978-94-6463-238-5_117How to use a DOI?
Keywords
vulnerability detection; Neural network; Expert rules; Fusion Features
Abstract

Software security flaw is one of the most important security problems nowadays. It may cause incalculable loss. However, today’s vulnerability detection technologies mostly rely on a single method, such as expert rules or deep learning, which has low scalability and fails to achieve better detection effect in the face of complex situations. In order to achieve better results of vulnerability detection, this paper proposes a vulnerability detection method named MF-TD based on the combination of neural network and expert rules and fusion of two characteristics. This method uses the combination of expert rules to highlight the semantic relation information, deeply understand the code logic structure based on the expert rules, and use the operation diagram to capture the statistical form and internal relation between the codes, and finally fuse the features for detection. The effectiveness of MF-TD was demonstrated in two different data sets.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)
Series
Advances in Intelligent Systems Research
Publication Date
26 September 2023
ISBN
978-94-6463-238-5
ISSN
1951-6851
DOI
10.2991/978-94-6463-238-5_117How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shuai Liu
AU  - Guan Wang
PY  - 2023
DA  - 2023/09/26
TI  - Research on Deep Learning Vulnerability Detection Method Based on Fusion Features
BT  - Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)
PB  - Atlantis Press
SP  - 909
EP  - 914
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-238-5_117
DO  - 10.2991/978-94-6463-238-5_117
ID  - Liu2023
ER  -