Explainable AI for Anomaly Detection in IoT Networks Using XGBoost

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R Raskhith Kumar; Sagar A; Thomas Sunil; V Saketh Nivesh; Manjunath P V

Explainable AI for Anomaly Detection in IoT Networks Using XGBoost

Authors: R Raskhith Kumar, Sagar A, Thomas Sunil, V Saketh Nivesh, Manjunath P V

Unique Paper ID: 178511
Volume: 11
Issue: 12
PageNo: 8533-8536

Keywords: Anomaly Detection Explainable AI SHAP LIME IoT Security XGBoost Surrogate Models Cybersecurity Network Intrusion Detection Machine Learning.

Abstract:
This paper introduces a holistic approach for identifying anomalies in Internet of Things (IoT) networks utilizing the robust XGBoost classification model and explainable artificial intelligence (XAI) methods. We leverage SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and a surrogate decision tree to supplement the model's interpretability. The performance of our method is tested on the IoT-23 dataset, which covers a variety of attack vectors as well as benign network traffic. The outcomes illustrate exceptional predictive accuracy as well as substantially improved model transparency, thereby enhancing understanding and confidence in automated systems for network security.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 8533-8536

Explainable AI for Anomaly Detection in IoT Networks Using XGBoost

Available:https://ijirt.org/article?manuscript=178511

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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