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An Improved Software Source Code Vulnerability Detection Method: Combination of Multi-Feature Screening and
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Sensors (Basel, Switzerland)
|April 28, 2025
Summary
This study introduces a new model for software vulnerability detection, improving accuracy and reducing training time. The multi-feature screening and integrated sampling model (MFISM) enhances software security by addressing dataset imbalance.
Area of Science:
- Computer Science
- Software Engineering
- Cybersecurity
Background:
- Software vulnerability detection is critical for security.
- Existing methods struggle with imbalanced datasets and lengthy training.
- Need for efficient and accurate vulnerability detection models.
Purpose of the Study:
- To introduce the Multi-Feature Screening and Integrated Sampling Model (MFISM) for enhanced software vulnerability detection.
- To improve the efficiency and accuracy of identifying vulnerabilities in source code.
- To overcome challenges of class imbalance and long training times in existing models.
Main Methods:
- Utilized Abstract Syntax Tree (AST) for feature extraction.
- Applied Analysis of Variance (ANOVA) and feature selection techniques.
- Implemented integrated over-sampling and outlier detection for data balancing.
- Employed a Bidirectional Long Short-Term Memory (Bi-LSTM) network for classification.
Main Results:
- MFISM improved F1 score by approximately 10% over DeepBalance.
- Reduced model training time to 2-3 hours.
- Demonstrated superior performance in source code vulnerability detection.
Conclusions:
- MFISM effectively enhances vulnerability detection accuracy and efficiency.
- The model successfully addresses class imbalance and reduces training duration.
- MFISM offers a superior solution for secure software development.
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