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https://github.com/cgcl-codes/vuldeepecker

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

https://github.com/cgcl-codes/vuldeepecker

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (5.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Basic Info
  • Host: GitHub
  • Owner: CGCL-codes
  • License: apache-2.0
  • Language: C
  • Default Branch: master
  • Homepage:
  • Size: 22.3 MB
Statistics
  • Stars: 326
  • Watchers: 16
  • Forks: 108
  • Open Issues: 10
  • Releases: 0
Created over 8 years ago · Last pushed over 5 years ago
Metadata Files
Readme License

README.md

Database of "VulDeePecker: A Deep Learning-Based System for Vulnerability Detection" (NDSS'18)

Code Gadget Database (CGD) focuses on two types of vulnerabilities in C/C++ programs, buer error vulnerability (CWE-119) and resource management error vulnerability (CWE-399). Each code gadget is composed of a number of program statements (i.e., lines of code), which are related to each other according to the data ow associated to the arguments of some library/API function calls.

Based on the National Vulnerability Database (NVD) and the NIST Software Assurance Reference Dataset (SARD) project, we collect 520 open source software program files with corresponding diff files and 8,122 test cases for the buer error vulnerability, and 320 open source software program files with corresponding diff files and 1,729 test cases for the resource management error vulnerability.

In total, the CGD database contains 61,638 code gadgets, including 17,725 code gadgets that are vulnerable and 43,913 code gadgets that are not vulnerable. Among the 17,725 code gadgets that vulnerable, 10,440 corresponds to buer error vulnerabilities and the rest 7,285 corresponds to resource management error vulnerabilities.

An extension of VulDeePecker: SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities (https://arxiv.org/abs/1807.06756)

The source code is published in https://github.com/SySeVR/SySeVR.

Owner

  • Name: CGCL-codes
  • Login: CGCL-codes
  • Kind: organization

CGCL/SCTS/BDTS Lab

GitHub Events

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Last Year
  • Watch event: 36
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