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Open source software security vulnerability detection based on dynamic behavior features.

Yuancheng Li1, Longqiang Ma1, Liang Shen2

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This study introduces a novel method for detecting open source software vulnerabilities using dynamic behavior features and an improved CNN-IndRNN model. The approach addresses limitations of source code analysis, enhancing security detection accuracy.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Open source software (OSS) offers flexibility but poses security risks.
  • Current source code-based vulnerability analysis methods suffer from inaccuracies like false positives and negatives.
  • There is a need for more effective OSS vulnerability detection techniques.

Purpose of the Study:

  • To propose a novel method for detecting open source software vulnerabilities using dynamic behavior features.
  • To address the limitations of traditional source code analysis in vulnerability detection.
  • To enhance the accuracy and reliability of open source software security analysis.

Main Methods:

  • Investigated the correlation between open source software vulnerabilities and API call sequences.
  • Developed a behavioral risk vulnerability database to support detection.
  • Constructed a Convolutional Neural Network-Independently Recurrent Neural Network (CNN-IndRNN) classification model, improving the IndRNN algorithm for vulnerability detection.

Main Results:

  • The proposed method effectively utilizes dynamic behavior features for vulnerability detection.
  • The improved CNN-IndRNN model demonstrated strong performance in classifying open source software security vulnerabilities.
  • Experimental results validated the effectiveness of the dynamic behavior-based approach.

Conclusions:

  • Dynamic behavior features offer a promising alternative for open source software vulnerability detection.
  • The developed CNN-IndRNN model enhances the capability for accurate security vulnerability identification in OSS.
  • This research contributes to improving the security posture of open source software.