FedDef: Robust Federated Learning-based Network Intrusion Detection Systems Against Gradient Leakage. (arXiv:2210.04052v1 [cs.CR])

Deep learning methods have been widely applied to anomaly-based network
intrusion detection systems (NIDS) to detect malicious traffic. To expand the
usage scenarios of DL-based methods, the federated learning (FL) framework
allows intelligent techniques to jointly train a model by multiple individuals
on the basis of respecting individual data privacy. However, it has not yet
been systematically evaluated how robust FL-based NIDSs are against existing
privacy attacks under existing defenses. To address this issue, in this paper
we propose two …

detection federated learning intrusion intrusion detection network systems