Software Vulnerability Detection Using Informed Code Graph Pruning

被引：0

作者：

Gear, Joseph ^{[1
]}

Xu, Yue ^{[1
]}

Foo, Ernest ^{[2
]}

Gauravaram, Praveen ^{[3
]}

Jadidi, Zahra ^{[2
]}

Simpson, Leonie ^{[1
]}

机构：

[1] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4000, Australia

[2] Griffith Univ, Sch Informat & Commun Technol, Brisbane, Qld 4111, Australia

[3] Tata Consultancy Serv Ltd TCS, Cyber Secur Res & Innovat, Brisbane, QLD 4000, Australia

来源：

IEEE ACCESS | 2023年 / 11卷

关键词：

Code representation; deep learning; source code semantics; vulnerability detection;

D O I：

10.1109/ACCESS.2023.3338162

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

pruning methods that can be used to reduce graph size to manageable levels by removing information irrelevant to vulnerabilities, while preserving relevant information. We present "Semantic-enhanced Code Embedding for Vulnerability Detection" (SCEVD), a deep learning model for vulnerability detection that seeks to fill these gaps by using more detailed information about code semantics to select vulnerability-relevant features from code graphs. We propose several heuristic-based pruning methods, implement them as part of SCEVD, and conduct experiments to verify their effectiveness. Our heuristic-based pruning improves on vulnerability detection results by up to 12% over the baseline pruning method.

引用

页码：135626 / 135644

页数：19

共 50 条

[21] Securing Code With Context: Enhancing Vulnerability Detection Through Contextualized Graph Representations
Rozi, Muhammad Fakhrur
Ban, Tao
Ozawa, Seiichi
Yamada, Akira
Takahashi, Takeshi
Inoue, Daisuke
IEEE ACCESS, 2024, 12 : 142101 - 142126
[22] Source Code Vulnerability Detection Based on Residual Gated Graph Convolutional Networks
Zhang, Jun
Li, Shanshan
Li, Lei
Wang, Haoyu
Computer Engineering and Applications, 2023, 59 (22) : 293 - 299
[23] Source Code Vulnerability Detection Based on Joint Graph and Multimodal Feature Fusion
Jin, Dun
He, Chengwan
Zou, Quan
Qin, Yan
Wang, Boshu
ELECTRONICS, 2025, 14 (05):
[24] Towards a Software Vulnerability Prediction Model using Traceable Code Patterns and Software Metrics
Sultana, Kazi Zakia
PROCEEDINGS OF THE 2017 32ND IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE'17), 2017, : 1022 - 1025
[25] Vulnerability Detection for Source Code Using Contextual LSTM
Xu, Aidong
Dai, Tao
Chen, Huajun
Ming, Zhe
Li, Weining
2018 5TH INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI), 2018, : 1225 - 1230
[26] Automated Software Vulnerability Detection in Statement Level using Vulnerability Reports
Mim, Rabaya Sultana
Ahammed, Toukir
Sakib, Kazi
PROCEEDINGS OF 2024 28TH INTERNATION CONFERENCE ON EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING, EASE 2024, 2024, : 454 - 455
[27] An Empirical Study on Vulnerability Detection for Source Code Software based on Deep Learning
Lin, Wei
Cai, Saihua
2021 21ST INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C 2021), 2021, : 1159 - 1160
[28] Graph-of-Code: Semantic Clone Detection Using Graph Fingerprints
Alhazami, Essa A.
Sheneamer, Abdullah M.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2023, 49 (08) : 3972 - 3988
[29] Software Vulnerability Detection using Large Language Models
Das Purba, Moumita
Ghosh, Arpita
Radford, Benjamin J.
Chu, Bill
2023 IEEE 34TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING WORKSHOPS, ISSREW, 2023, : 112 - 119
[30] Combining Graph-Based Learning with Automated Data Collection for Code Vulnerability Detection
Wang, Huanting
Ye, Guixin
Tang, Zhanyong
Tan, Shin Hwei
Huang, Songfang
Fang, Dingyi
Feng, Yansong
Bian, Lizhong
Wang, Zheng
IEEE Transactions on Information Forensics and Security, 2021, 16 : 1943 - 1958

← 1 2 3 4 5 →