DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection
The advances of machine learning (ML) including deep learning (DL) have enabled several approaches to implicitly learn vulnerable code patterns to automatically detect software vulnerabilities. A recent study showed that despite successes, the existing ML/DL-based vulnerability detection (VD) models are limited in the ability to distinguish between the two classes of vulnerability and benign code. We propose DeepVD, a graph-based neural network VD model that emphasizes on class-separation features between vulnerability and benign code. DeepVD leverages three types of class-separation features at different levels of abstraction: statement types (similar to Part-of-Speech tagging), Post-Dominator Tree (covering regular flows of execution), and Exception Flow Graph (covering the exception and error-handling flows). We conducted several experiments to evaluate DeepVD in a real-world vulnerability dataset of 303 projects with 13,130 vulnerable methods. Our results show that DeepVD relatively improves over the state-of-the-art ML/DL-based VD approaches 13%–29.6% in precision, 15.6%–28.9% in recall, and 16.4%–25.8% in F-score. Our ablation study confirms that our designed features and components help DeepVD achieve high class-separability for vulnerability and benign code.
Fri 19 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Vulnerability detectionTechnical Track / Journal-First Papers at Meeting Room 106 Chair(s): Cuiyun Gao Harbin Institute of Technology | ||
13:45 15mTalk | An Empirical Study of Deep Learning Models for Vulnerability Detection Technical Track Benjamin Steenhoek Iowa State University, Md Mahbubur Rahman Iowa State University, Richard Jiles Iowa State University, Wei Le Iowa State University Pre-print | ||
14:00 15mTalk | DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection Technical Track Wenbo Wang New Jersey Institute of Technology, Tien N. Nguyen University of Texas at Dallas, Shaohua Wang New Jersey Institute of Technology, Yi Li New Jersey Institute of Technology, Jiyuan Zhang University of Illinois Urbana-Champaign, Aashish Yadavally The University of Texas at Dallas Pre-print | ||
14:15 15mTalk | Enhancing Deep Learning-based Vulnerability Detection by Building Behavior Graph Model Technical Track Bin Yuan Huazhong University of Science and Technology, Yifan Lu Huazhong University of Science and Technology, Yilin Fang Huazhong University of Science and Technology, Yueming Wu Nanyang Technological University, Deqing Zou Huazhong University of Science and Technology, Zhen Li Huazhong University of Science and Technology, Zhi Li Huazhong University of Science and Technology, Hai Jin Huazhong University of Science and Technology | ||
14:30 15mTalk | Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning Technical Track Xin-Cheng Wen Harbin Institute of Technology, Yupan Harbin Institute of Technology, Cuiyun Gao Harbin Institute of Technology, Hongyu Zhang The University of Newcastle, Jie M. Zhang King's College London, Qing Liao Harbin Institute of Technology | ||
14:45 15mTalk | Does data sampling improve deep learning-based vulnerability detection? Yeas! and Nays! Technical Track Xu Yang University of Manitoba, Shaowei Wang University of Manitoba, Yi Li New Jersey Institute of Technology, Shaohua Wang New Jersey Institute of Technology Pre-print | ||
15:00 7mTalk | Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data Journal-First Papers Aayush Garg University of Luxembourg, Luxembourg, Renzo Degiovanni SnT, University of Luxembourg, Matthieu Jimenez SnT, University of Luxembourg, Maxime Cordy University of Luxembourg, Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg Link to publication DOI Authorizer link Pre-print Media Attached | ||
15:07 7mTalk | Do I really need all this work to find vulnerabilities? An empirical case study comparing vulnerability detection techniques on a Java application Journal-First Papers Sarah Elder North Carolina State University, Nusrat Zahan North Carolina State University, Rui Shu North Carolina State University, Valeri Kozarev North Carolina State University, Tim Menzies North Carolina State University, Laurie Williams North Carolina State University | ||