Enhancing Security Testing Through Evolutionary Techniques: A Novel Model

Abstract

Software systems are integral to modern organizations, necessitating rigorous testing to ensure security and integrity. However, with the evolution of technology, vulnerabilities and threats to software security are on the rise. Metaheuristic algorithms (MHS) or evolutionary techniques have emerged as valuable tools in addressing these challenges. This research aims to explore and evaluate evolutionary software security testing techniques comprehensively. Specific objectives include analyzing different test cases and strategies, identifying commonly targeted security vulnerabilities, assessing cost-effective and scalable testing techniques, and developing a framework for selecting optimal evolutionary testing methods. The methodology employs a systematic literature review across five major databases, selecting 52 relevant papers. Findings indicate prevalent security vulnerabilities such as Cross-site scripting XSS, Buffer overflow/stack overflow, SQL/XML injection, etc. The commonly used genetic algorithms for software security testing are Genetic algorithm, Particle swarm optimization, and Simulated annealing. Cost-effective and scalable MHS algorithms are ranked, with the Genetic algorithm emerging as the most effective. Additionally, a model for selecting and utilizing MHS algorithms is proposed based on research findings. This study offers valuable insights for researchers and practitioners, outlining future research avenues and providing practical guidelines for employing MHS algorithms in software security testing.