AIBullisharXiv – CS AI · 7h ago6/10
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Improving IoT Intrusion Detection Through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data
Researchers address critical class imbalance problems in IoT intrusion detection by applying SMOTE oversampling to power-based side-channel datasets, achieving superior detection performance with Random Forest and Extra Trees algorithms. The study demonstrates that balanced datasets reveal minority attack classes previously missed by traditional evaluation metrics, advancing security for IoT networks.