Enhancing Network Security Through Explainable AI: A Comparative Study of LIME and SHAP
Keywords:
Intrusion Detection System, Explainable Artificial Intelligence, Network Security, Machine Learning, Multi-Layer Perceptron, LIME, SHAP, Model Interpretability, Feature Importance, Cybersecurity, Anomaly Detection, Classification.Abstract
Intrusion Detection Systems (IDS) play acrucial role in protecting modern network infrastructuresagainst increasingly sophisticated cyber threats. With thegrowing adoption of machine learning techniques, particularly Multi-Layer Perceptrons (MLPs), IDS haveimproved their ability to identify complex patterns innetwork traffic and detect malicious activities
References
1. Ribeiro, M. T., Singh, S., & Guestrin, C., "Why Should I Trust You? Explaining the Predictions of Any Classifier", 2016, https://arxiv.org/abs/1602.04938
2. Lundberg, S. M., & Lee, S. I., "A Unified Approach to Interpreting Model Predictions", 2017, https://arxiv.org/abs/1705.07874
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Published
2025-12-15
How to Cite
Dr.T.Prasad,Dr. M. Sree Ram Kiran Nag. (2025). Enhancing Network Security Through Explainable AI: A Comparative Study of LIME and SHAP . Journal of Computational Analysis and Applications (JoCAAA), 34(12), 1222–1228. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5891
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