Are Source Code Metrics “Good Enough” in Predicting Security Vulnerabilities?

Abstract

Modern systems produce and handle a large volume of sensitive enterprise data. Therefore, security vulnerabilities in the software systems must be identified and resolved early to prevent security breaches and failures. Predicting security vulnerabilities is an alternative to identifying them as developers write code. In this study, we studied the ability of several machine learning algorithms to predict security vulnerabilities. We created two datasets containing security vulnerability information from two open-source systems: (1) Apache Tomcat (versions 4.x and five 2.5.x minor versions). We also computed source code metrics for these versions of both systems. We examined four classifiers, including Naive Bayes, Decision Tree, XGBoost Classifier, and Logistic Regression, to show their ability to predict security vulnerabilities. Moreover, an ensemble learner was introduced using a stacking classifier to see whether the prediction performance could be improved. We performed cross-version and cross-project predictions to assess the effectiveness of the best-performing model. Our results showed that the XGBoost classifier performed best compared to other learners, i.e., with an average accuracy of 97% in both datasets. The stacking classifier performed with an average accuracy of 92% in Struts and 71% in Tomcat. Our best-performing model—XGBoost—could predict with an average accuracy of 87% in Tomcat and 99% in Struts in a cross-version setup.

Keywords

• prediction
• security vulnerabilities
• machine learning
• source code
• software metrics