An MLJAR AutoML stacked ensemble achieves 90% accuracy and 89% F1 on binary NSL-KDD intrusion detection, beating four single models in the authors' comparison.
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An AutoML-based approach for Network Intrusion Detection
An MLJAR AutoML stacked ensemble achieves 90% accuracy and 89% F1 on binary NSL-KDD intrusion detection, beating four single models in the authors' comparison.