Vulnerability Coverage for Secure Configuration

TL;DR

This paper introduces vulnerability coverage as a new adequacy testing approach that uses evolutionary algorithms and CVSS scores to identify security vulnerabilities in software, enhancing testing thoroughness.

Contribution

It proposes a novel vulnerability coverage measure and adapts genetic algorithms and particle swarm optimization for effective vulnerability testing.

Findings

Uses CVSS as a fitness measure for test input generation

Employs evolutionary algorithms to identify vulnerability patterns

Enhances software security testing coverage

Abstract

We present a novel idea on adequacy testing called ``{vulnerability coverage}.'' The introduced coverage measure examines the underlying software for the presence of certain classes of vulnerabilities often found in the National Vulnerability Database (NVD) website. The thoroughness of the test input generation procedure is performed through the adaptation of evolutionary algorithms namely Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The methodology utilizes the Common Vulnerability Scoring System (CVSS), a free and open industry standard for assessing the severity of computer system security vulnerabilities, as a fitness measure for test inputs generation. The outcomes of these evolutionary algorithms are then evaluated in order to identify the vulnerabilities that match a class of vulnerability patterns for testing purposes.