Neuro evolutionary swarm intelligence framework for real time anomaly detection in IoT enabled industrial networks

Anomaly detection remains a critical challenge for industrial Internet of Things networks, where the need for rapid identification of abnormal behaviour must be balanced against constraints on accuracy and adaptability. This research addresses the persistent gap in achieving sub-second response times without sacrificing detection precision by proposing a novel neuro evolutionary swarm intelligence framework. In this framework, time-series sensor data are first encoded into compact feature vectors and then passed through a recurrent neural network whose weights and hyperparameters were co-optimized using a genetic algorithm informed by particle swarm dynamics. During deployment, sliding-window retraining and dynamic neighbourhood adaptation enable the model to maintain sensitivity to rare deviations while reducing false alarms. Experiments conducted on a large-scale simulated industrial control system demonstrated that the proposed approach achieved 97.3 percent detection accuracy with an average latency of 0.85 seconds, outperforming baseline LSTM and pure PSO–GA hybrids by over 8 percent. These results confirm that integrating evolutionary computing and swarm intelligence techniques delivers a robust, real-time anomaly-detection solution for smart industrial environments.