An Explainable AI-Driven Intrusion Detection System Using Optimized  Gradient Boosting for Real-Time Cyber Threat Intelligence

Ritu Raj Kashyap, Abhay Shukla, Somendra Tripathi

An Explainable AI-Driven Intrusion Detection System Using Optimized Gradient Boosting for Real-Time Cyber Threat Intelligence

Authors

Ritu Raj Kashyap, Abhay Shukla, Somendra Tripathi

Keywords:

Intrusion Detection System, Explainable Artificial Intelligence, XGBoost, SHAP, Cybersecurity, Machine Learning, CICIDS2017, Network Security

Abstract

The rapid evolution of cyber threats has significantly increased the demand for intelligent, adaptive,and interpretable intrusion detection systems (IDS). Traditional intrusion detection approaches,particularly signature-based systems, are limited in detecting zero-day and evolving cyber-attacks.Although machine learning-based IDS frameworks have demonstrated improved detection capabilities, their lack of interpretability restricts their deployment in critical cybersecurityinfrastructures where transparency and accountability are essential.

References

Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.

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Published

2024-12-20

How to Cite

Ritu Raj Kashyap, Abhay Shukla, Somendra Tripathi. (2024). An Explainable AI-Driven Intrusion Detection System Using Optimized Gradient Boosting for Real-Time Cyber Threat Intelligence . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8315–8321. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5299