SHERLOCK: A Deep Learning Approach To Detect Software Vulnerabilities

TL;DR

This paper introduces SHERLOCK, a deep learning model using CNNs to improve software vulnerability detection accuracy over traditional static and dynamic analysis methods.

Contribution

It presents a novel CNN-based approach for function-level vulnerability detection, demonstrating high accuracy and effectiveness for specific CWE categories.

Findings

Successfully detects multiple vulnerabilities with high accuracy

Performs well on CWE-199, CWE-120, and CWE-Other

Less reliable for some vulnerabilities due to dataset limitations

Abstract

The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional software vulnerability detection techniques, such as static and dynamic analysis, have been shown to be ineffective at detecting multiple vulnerabilities. To address this issue, this study employed a deep learning approach, specifically Convolutional Neural Networks (CNN), to solve the software vulnerability detection problem. A 5-split cross-validation approach was used to train and evaluate the CNN model, which takes tokenized source code as input. The findings indicated that Sherlock successfully detected multiple vulnerabilities at the function level, and its performance was particularly strong for CWE-199, CWE-120, and CWE-Other, with an overall high accuracy rate and significant true positive and true negative values. However, the performance was less reliable for some vulnerabilities due to the lack of a standardized dataset which will be a future research direction. The results suggest that compared to current techniques, the proposed deep learning approach has the potential to substantially enhance the accuracy of software vulnerability detection.