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Software Vulnerability Detection Using Informed Code Graph Pruning
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DOI：10.1109/ACCESS.2023.3338162.png)
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Abstract
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pruning methods that can be used to reduce graph size to manageable levels by removing information irrelevant to vulnerabilities, while preserving relevant information. We present Semantic-enhanced Code Embedding for Vulnerability Detection (SCEVD), a deep learning model for vulnerability detection that seeks to fill these gaps by using more detailed information about code semantics to select vulnerability-relevant features from code graphs. We propose several heuristic-based pruning methods, implement them as part of SCEVD, and conduct experiments to verify their effectiveness. Our heuristic-based pruning improves on vulnerability detection results by up to 12% over the baseline pruning method.
Keywords:
Code representation
deep learning
source code semantics
vulnerability detection
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29.4W
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