VULNERLIZER: Cross-analysis Between Vulnerabilities and Software Libraries

TL;DR

VULNERLIZER is a novel framework that leverages CVE and software library data with clustering and model training to predict vulnerable libraries, achieving over 75% accuracy.

Contribution

It introduces a new cross-analysis framework combining clustering and model training to link vulnerabilities with software libraries.

Findings

Prediction accuracy exceeds 75%.

Effective in predicting future vulnerable libraries.

Demonstrates potential for proactive vulnerability management.

Abstract

The identification of vulnerabilities is a continuous challenge in software projects. This is due to the evolution of methods that attackers employ as well as the constant updates to the software, which reveal additional issues. As a result, new and innovative approaches for the identification of vulnerable software are needed. In this paper, we present VULNERLIZER, which is a novel framework for cross-analysis between vulnerabilities and software libraries. It uses CVE and software library data together with clustering algorithms to generate links between vulnerabilities and libraries. In addition, the training of the model is conducted in order to reevaluate the generated associations. This is achieved by updating the assigned weights. Finally, the approach is then evaluated by making the predictions using the CVE data from the test set. The results show that the VULNERLIZER has a great potential in being able to predict future vulnerable libraries based on an initial input CVE entry or a software library. The trained model reaches a prediction accuracy of 75% or higher.