VULDetect: A Lightweight Ensemble Based Framework for Automated Software System Vulnerability Detection

This paper presents VULDetect: a lightweight and highly accurate ensemble based software system vulnerability detection framework trained on a large C/C++ file corpus comprising thousands of vulnerable and non-vulnerable source code snippets. VULDetect uses a deep learning based ensemble model comprising multiple classifiers to perform a comprehensive vulnerability assessment of the software programs. Experimental results on a proprietary dataset and a benchmark DIVERSEVUL dataset show that VULDetect is capable of detecting wide range of software system vulnerabilities with high accuracy and low false positive rate. In addition, VULDetect is also highly compact and lightweight in comparison to other Large Language Model (LLM) based frameworks proposed in the literature, which makes it highly suitable for deployment on computation constrained edge devices.