Enhancing Network Security: Robust Anomaly Detection with Ensemble Learning and Explainable AI
Ibrahim Gad, Abubakr Al-Ashmali, Manjaiah D.H, Hasan A. Hashim, Abdulqader M. Almars, El-Sayed Atlam · 2025
Network security is a critical issue in the digital era, as the complexity and intelligence of cyber threats pose significant risks to people as well as companies. Traditional anomaly detection techniques often struggle to effectively identify novel and evasive network attacks, prompting the need for more robust and adaptive approaches. This study presents a robust framework for enhancing network security through the integration of ensemble learning and explainable artificial intelligence (XAI) techniques. The proposed approach utilizes the combined strengths of ensemble learning models to enhance the performance and reliability of network anomaly detection. The proposed model achieves high scores across all key metrics: precision (99.99%), accuracy (99.99%), recall (100%), and F1-score (99.99%). Furthermore, the XAI techniques provide valuable insights into the fundamental elements influencing the detection decisions.