Lightweight and Explainable AI-Driven Intrusion Detection for IoT Networks: A Resource-Efficient Machine Learning Framework with SHAP-Based Interpretation

This paper presents a lightweight intrusion detection framework for IoT networks that combines machine learning-based detection with explainable AI analysis. The study evaluates several low-complexity classifiers, including Logistic Regression, Decision Tree, Random Forest, Gaussian Naive Bayes, and LightGBM, to identify a suitable model for resource-limited IoT environments. The proposed approach incorporates feature optimization, leakage-aware preprocessing, class balancing, and memory-efficient data handling to reduce computational requirements while maintaining detection capability. In addition, SHAP-based analysis is applied to explain the factors influencing intrusion decisions, improving the transparency of the detection process. The framework is evaluated using the CICIoT2023 DDoS-ICMP Fragmentation dataset, demonstrating how lightweight machine learning models can be used for accurate and interpretable IoT security monitoring.