Reusing a frozen pretrained autoencoder, retrained clustering, and fine-tuned XGBoost on ACI-IoT-2023 outperforms FcNN and 1D-CNN with about half the training data, and metamodel-based UQ outperforms score-based UQ on both datasets.
An intrusion-detection model.IEEE Transactions on software engineering, SE-13(2):222– 232, 1987
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Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning
Reusing a frozen pretrained autoencoder, retrained clustering, and fine-tuned XGBoost on ACI-IoT-2023 outperforms FcNN and 1D-CNN with about half the training data, and metamodel-based UQ outperforms score-based UQ on both datasets.