What question did this study set out to answer?

This research aims to develop an efficient framework for detecting anomalies in IoT networks using edge AI technology.

May 19, 20260 citationsOpen Access

Lightweight Explainable Edge AI Framework for IoT Network Anomaly Detection

Key Points

This research aims to develop an efficient framework for detecting anomalies in IoT networks using edge AI technology.
Integrated machine learning techniques with explainable AI methods like SHAP and LIME.
Designed for real-time detection on resource-constrained edge devices.
Evaluated against traditional models like SVM and LSTM.
Achieved improved accuracy in anomaly detection compared to traditional models.
Demonstrated reduced latency in the detection process.
Showed better efficiency in resource utilization on edge devices.

Abstract

This work presents a lightweight explainable Edge AI framework for IoT network anomaly detection. The proposed model is designed to improve real-time detection performance on resource-constrained edge devices. It integrates machine learning techniques with explainable AI methods such as SHAP and LIME to enhance interpretability and trust. Experimental evaluation shows improved accuracy, reduced latency, and better efficiency compared to traditional machine learning models such as SVM and LSTM. The framework is suitable for deployment in real-world IoT environments requiring low-latency and high-security monitoring.

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