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Context and Multi-Features-Based Vulnerability Detection: A Vulnerability Detection Frame Based on Context Slicing

Yulin Zhang1, Yong Hu1, Xiao Chen1

  • 1School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China.

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|March 13, 2024
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Summary
This summary is machine-generated.

This study introduces Context and Multi-Features-based Vulnerability Detection (CMFVD), a new framework for identifying software security vulnerabilities. CMFVD effectively detects flaws by analyzing code dependencies, achieving high accuracy.

Keywords:
context slicinggraph neural networkmulti-featuresvulnerability detection

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Increasing reliance on open-source libraries and secondary development introduces software security vulnerabilities.
  • Current source code vulnerability detection methods often neglect complex programming language dependencies.

Purpose of the Study:

  • To propose a novel framework, Context and Multi-Features-based Vulnerability Detection (CMFVD), for enhanced software vulnerability detection.
  • To address limitations of existing methods by integrating source code graphs and textual sequences.

Main Methods:

  • Developed the Context and Multi-Features-based Vulnerability Detection (CMFVD) framework.
  • Utilized a novel Context Slicing method to capture contextual information.
  • Integrated graph convolutional networks (GCNs) and bidirectional gated recurrent units (BGRUs) with attention mechanisms for feature extraction.

Main Results:

  • CMFVD achieved the highest F1-score of 0.986 on the Software Assurance Reference Datasets (SARDs).
  • The framework demonstrated superior performance compared to existing models in vulnerability detection.
  • Effectively extracted local semantic and syntactic information from source code.

Conclusions:

  • CMFVD provides a promising approach for identifying and rectifying security flaws in large-scale codebases.
  • The integration of graph and sequence-based methods enhances vulnerability detection accuracy.
  • Highlights the importance of considering code dependencies in vulnerability analysis.