Explainable Artificial Intelligence for Real-Time Intrusion Detection in Software-Defined Networks
Abstract
The increasing complexity of modern communication networks has led to sophisticated cyber-attacks that traditional intrusion detection systems struggle to detect efficiently. Deep learning models provide superior detection performance but often operate as black-box systems, limiting trust among cybersecurity professionals. This study presents an Explainable Artificial Intelligence (XAI)-based intrusion detection framework for Software-Defined Networks (SDNs). The proposed model combines Graph Neural Networks (GNNs) with Transformer-based feature extraction to classify malicious network traffic while integrating SHAP and LIME explanation techniques to interpret detection decisions. The framework is evaluated on benchmark cybersecurity datasets using multiple performance metrics, including accuracy, precision, recall, F1-score, and detection latency. Experimental results demonstrate improved attack detection capability with transparent decision explanations, enabling network administrators to better understand threat behavior. The proposed system enhances trust, accountability, and operational effectiveness in intelligent network security management.
References
Molli, S. M. (2023). Self-Evolving Multi-Agent Systems for Autonomous Task Planning and Decision Intelligence. Australian Journal of Modern Research & Applications, 6(6).
Molli, S. M. (2023). Accelerator-Optimized Infrastructure for Training and Serving Next-Generation Multimodal Foundation Models. Australian Journal of Cross-Disciplinary Innovation, 5(5).
Konda, P. R. (2023). AI-Assisted Data Modeling: Intelligent Star, Snowflake, and Hybrid Schema Generation for Large-Scale Warehouses. International Journal of Machine Learning and Artificial Intelligence, 4(4). https://jmlai.in/index.php/ijmlai/article/view/90
Ensuring BI Reporting Accuracy Using AI-Based Back-Tracing of Metrics to ETL Lineage and Data Marts. (2023). International Machine Learning Journal and Computer Engineering, 6(6). https://mljce.in/index.php/Imljce/article/view/69
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 12:1–12:19.
Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 4765–4774.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542(7639), 115–118.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347–1358.
Seneviratne, S., Hu, Y., Nguyen, T., et al. (2017). A Survey of Wearable Devices and Challenges. IEEE Communications Surveys & Tutorials, 19(4), 2573–2620.
Dorri, A., Kanhere, S. S., Luo, X., & Jurdak, R. (2017). Blockchain for IoT Security and Privacy: The Case Study of a Smart Home. Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 618–623.
