Z
Zhen Li
Researcher at Huazhong University of Science and Technology
Publications - 35
Citations - 1574
Zhen Li is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: National Vulnerability Database & Deep learning. The author has an hindex of 9, co-authored 33 publications receiving 715 citations. Previous affiliations of Zhen Li include Hebei University.
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Proceedings ArticleDOI
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
TL;DR: The study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features and Experimental results show that VulDeePecker can achieve much fewer false negatives and reasonable false positives than other approaches.
Proceedings ArticleDOI
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
TL;DR: Zhang et al. as discussed by the authors proposed using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other.
Journal ArticleDOI
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
TL;DR: This work proposes the first systematic framework for using deep learning to detect vulnerabilities, dubbed Syntax- based, Semantics-based, and Vector Representations (SySeVR), which focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities.
Proceedings ArticleDOI
VulPecker: an automated vulnerability detection system based on code similarity analysis
TL;DR: Vulnerability Pecker is presented, a system for automatically detecting whether a piece of software source code contains a given vulnerability or not, and experiments show that VulPecker detects 40 vulnerabilities that are not published in the National Vulnerability Database (NVD).
Journal ArticleDOI
$\mu$ μ VulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection
TL;DR: Experimental results show that $\mu$VulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-Dependence) can lead to higher detection capabilities.