A metric for software vulnerabilities classification

TL;DR

This paper proposes a new metric for classifying software vulnerabilities by combining existing methods with machine learning, tested on real-world data to improve vulnerability detection accuracy.

Contribution

It introduces a novel metric classification approach that integrates feature sets and machine learning models for vulnerability assessment.

Findings

Effective feature set identified for vulnerability classification

Machine learning models outperform traditional methods

Relation between classifier choice and features established

Abstract

Vulnerability discovery and exploits detection are two wide areas of study in software engineering. This preliminary work tries to combine existing methods with machine learning techniques to define a metric classification of vulnerable computer programs. First a feature set has been defined and later two models have been tested against real world vulnerabilities. A relation between the classifier choice and the features has also been outlined.