GeneticSecOps: Harnessing Heuristic Genetic Algorithms for Automated Security Testing and Vulnerability Detection in DevSecOps

This paper addresses security testing and vulnerability detection within the DevSecOps framework, emphasizing the need for automated tools to streamline and enhance security practices. Current challenges are identified, and related work is reviewed to establish context. Our Python-based solution offers two main components: an automated security testing tool for rigorous testing throughout the software development lifecycle and an automated vulnerability detection tool to identify potential vulnerabilities in system logs and network traffic. To optimize feature selection in the vulnerability detection tool, we employ the Genetic Algorithm (GA). The GA efficiently explores potential feature combinations, selecting the most relevant features to maximize vulnerability detection accuracy while minimizing the number of features. Simulated results validate the effectiveness of our solution in real-world scenarios, with the optimal solution achieving a fitness score of 0.982. By automating security testing and vulnerability detection, our approach enhances the security and reliability of software development. The paper concludes by discussing the implications of our findings and proposes future research directions. Embracing automation in security testing fortifies software applications and safeguards critical data from threats. Our proposed solution offers a promising avenue for advancing automated security testing and vulnerability detection within the DevSecOps paradigm.