Cybersecurity is crucial in today’s digital age to protect personal information. DoS and DDoS assaults overwhelm servers with excessive traffic, resulting in service outages and considerable financial losses. Their intricacy and size make identification and mitigation difficult for cybersecurity defenses. When it comes to identifying and mitigating the DoS and DDoS attacks, traditional solutions frequently fall short due to these intricate and constantly changing threats. This study offers a sophisticated method that finds optimal solutions in search space by utilizing meta-heuristics algorithms, and the computational techniques known as Particle Swarm Optimization (PSO) continuously improve solutions based on the movement and intelligence of birds and fish where it optimizes traffic patterns and resource allocation to detect and provide shortest path for DoS and DDoS attacks. Ant Colony Optimization (ACO) is an algorithm that simulates ants’ foraging behavior, finding the shortest paths by incrementally building solutions based on pheromone intensity, optimizing network routes and enhancing network resilience. The study investigates comparative analysis between the PSO and ACO thus providing superior capabilities in combating threats, how various hyperparameters affect the performance of each algorithm, and, via a thorough tuning procedure, determines the ideal parameter values. The results show that these characteristics have a major effect on the robustness and efficiency of the algorithms and also address the potential avenues for applying meta-heuristics to cybersecurity professionals, network administrators, and organizations aiming to enhance their defense mechanisms against sophisticated threads.