From text to threats: A language model approach to software vulnerability detection
Author:
Omar Marwan1, Burrell Darrell2
Affiliation:
1. 1 College of Computing, Illinois Institute of Technology , Capitol Technology University , Laurel, Maryland 20708 , USA 2. 2 College of Computing , Capitol Technology University , Laurel, Maryland 20708 , USA
Abstract
Abstract
In the rapidly-evolving landscape of software development, the detection of vulnerabilities in source code has become paramount. Our study introduces a novel knowledge distillation (KD) technique aimed at enhancing vulnerability detection in software codebases. Using benchmark datasets such as SARD, SeVC, Devign, and D2A, we assess the prowess of the KD method when applied to different classifiers, specifically GPT2, CodeBERT, and LSTM. The empirical results revealed a marked improvement in the performance of these classifiers upon the implementation of the KD technique, particularly with the GPT-2 model demonstrating the most promising outcomes. This work underscores the potential of integrating transformer-based learning models, like GPT-2, with knowledge distillation for more efficient and accurate vulnerability detection.
Publisher
Walter de Gruyter GmbH
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