Advanced Security in Distributed Web Systems Using Genetic Algorithm-Based Techniques

The growing sophistication of cyber threats has heightened the need for enhanced security measures in distributed web systems. This paper explores the potential of genetic algorithms to reinforce these systems by improving their adaptability to emerging vulnerabilities. Genetic algorithms, rooted in evolutionary principles, provide a mechanism to optimize security parameters, such as threat detection, intrusion response, and anomaly identification. Unlike traditional methods, these algorithms offer dynamic solutions capable of evolving with changing attack patterns. The study delves into various ways genetic algorithms can be incorporated into security frameworks for distributed systems, focusing on how these adaptive strategies could increase system resilience and robustness. Key considerations include the efficiency of configuration optimization, the ability to manage diverse security scenarios, and the scalability of algorithmic integration across multiple nodes within a system. By analyzing theoretical models and current advancements in evolutionary computing, we provide a comprehensive evaluation of the algorithm's potential impact on distributed security infrastructures. This research highlights the importance of innovative, adaptive approaches to maintaining the security of increasingly interconnected web systems in the face of evolving cyber risks.