FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems. (arXiv:2210.04052v2 [cs.CR] UPDATED)

Deep learning (DL) methods have been widely applied to anomaly-based network
intrusion detection system (NIDS) to detect malicious traffic. To expand the
usage scenarios of DL-based methods, the federated learning (FL) framework
allows multiple users to train a global model 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, we propose two privacy evaluation metrics
designed for …

defense detection federated learning intrusion intrusion detection network systems