Targeted fixes to AOC-IDS using balanced sampling, pseudo-label filtering, mixup, and a lighter autoencoder raise accuracy to 95.45% with XGBoost and 90.88% with deep learning on UNSW-NB15 while cutting parameters 55%.
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Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures
Targeted fixes to AOC-IDS using balanced sampling, pseudo-label filtering, mixup, and a lighter autoencoder raise accuracy to 95.45% with XGBoost and 90.88% with deep learning on UNSW-NB15 while cutting parameters 55%.