Wireless Sensor Networks (WSNs) are extensively dispersed networks of sensor nodes that use wireless communication to keep track of and collect environmental data. Conventional prevention-based approaches are ineffective detection-based strategies. While security remains a problem, and especially in view of emerging vulnerabilities like Black Hole Attacks. The analysis proposes Swarm-Enabled Intrusion Detection Systems (SE-IDS), which utilizes swarm intelligence concepts to efficiently detect and mitigate malicious behavior. SE-IDS maximizes detection accuracy and minimizes false positives by utilizing the collective knowledge of the network, securing network resources. Metaheuristics are approaches that guide the exploration phase. For identifying nearly-optimal solutions, the objective is to efficiently explore the search space. This research presents a unique heuristic method that combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) to mitigate attacks from Black holes in Wireless Sensor Networks (WSNs) and ensure secure and reliable communication. The findings indicate that ACO outperforms ABC in terms of computational efficiency and optimization. This study offers value to researchers, network engineers, and security practitioners as it offers insightful information about the way the ABC and ACO algorithms execute in Wireless Sensor Networks (WSNs), enhancing security features and efficiency.