network-intrusion-detection

A federated learning research project reveals that global accuracy can mask catastrophic failure on minority attack classes in network intrusion detection, showing that per-client performance and aggregation method choice are critical for rare attack detection.

This paper presents a rigorous N-qubit theory of stochastic quantum neural networks (SQNNs) for adversarially robust network intrusion detection, proving a decoherence-contraction theorem and showing that depolarising noise provides robustness against adversarial attacks, with experiments on the NSL-KDD dataset.

This paper introduces nCMD, a benign-anchored feature selection method for imbalanced network intrusion detection that scores features by deviation from benign class mean, outperforming classical filters on benchmark datasets.