Advanced Code Vulnerability Detection via Deep Learning
Explore how deep learning can enhance vulnerability detection in static analysis of code, overcoming traditional limitations by utilizing real-valued vector representations. Research focuses on constructing word embeddings for complex code syntax and structures for improved accuracy.
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Presentation Transcript
Vulnerability Detection Via Deep Learning Scholar: John Heaps Advisor: Dr. Jianwei Niu
Vulnerability Detection in Static Analysis Static analysis is one of the most popular techniques for code analysis and vulnerability detection Static analysis limitations: Requires specifications/patterns/rules to be provided for it Usually very conservative Not always scalable We believe deep learning can help overcome some of these limitations
Vulnerability Detection Using Deep Learning Goal: Given a segment of code, the code elements will be fed into a deep learning classifier that will determine if the code has a vulnerability or not To use deep learning on code, we take motivation from deep learning on natural language Hindle, Abram, et al. "On the naturalness of software." 2012 34th International Conference on Software Engineering (ICSE). IEEE, 2012. Deep learning algorithms do not understand text, only numbers So code elements must first be converted to a real-valued vector representation (called word embeddings )
Current Research Problems How to construct proper word embeddings for source code? Syntax and structure of code is much more complex than natural language How to best represent certain code elements, such as methods, expressions, data structures, etc.? We know of no robust, annotated dataset for code vulnerabilities for our deep learning model to learn on
