Hybrid Ensemble Learning with Explainable AI for Anomaly Detection in Network Traffic

The increasing intricacy and quantity of network data pose significant obstacles for anomaly detection systems. Detecting and responding appropriately to novel and changing dangers is a challenge for conventional approaches. This research presents a novel method for explainable AI (XAI)-integrated hybrid ensemble learning for improving real-time anomaly detection in network data. The proposed model combines many machines learning techniques, including Random Forest, deep learning (CNN, Bi-LSTM) and boosting, to provide a robust ensemble capable of identifying a wide range of irregularities. Furthermore, by employing XAI techniques, the model's decisions are rendered in a clear and understandable way, encouraging confidence and comprehension among cybersecurity specialists. The proposed technique is tested on two widespread network traffic datasets: CICIDS 2017 and UNSW-NB15. Experimental results demonstrate that our model achieves high accuracy rates of 99.98% and 97.71% on these datasets, respectively, with False Positive Rates (FPR) as low as 0.02% and 0.01%. Based on experimental out-comes, our model provides a dependable solution for network anomaly detection, outperforming current state-of-the-art methods in rapports of accuracy, precision, recall, and F1-score.