With advancement of information technology, intrusion is becoming more common in the internet era. The increased use of cloud services have also resulted in assaults on servers. In order to enhance network performance, this research offers a novel IDS (Intrusion Detection System) that can quickly identify large-scale server assaults in wireless networks. This work uses SHO (Spotted Hyena Optimisation) that mimics spotted hyena’s hunting behaviours. SHO, a swarm based meta-heuristics a method uses masses in solving issues and identifies important elements in server assaults. This optimisation algorithm improves detection and accuracy and is also used to detect web log hacking attacks and fake web pages. It outperforms other existing methods like SSO (Slap Swarm Optimisation), GWO (Grey Wolf Optimisation), and PSO (Particle Swarm Optimisation) algorithms in some applications. The goal of the experiment was to examine the suggested strategy using a common dataset. The suggested study appears to have produced notable outcomes for of F1 score, detection accuracy, and FAR (false alarm rate).