Adaptive and Proximal Federated Optimization for Transferable Network Intrusion Detection

Reported by arXiv cs.CR (Rezwan Islam Salvi, MD Rafin Jawad Hafiz, Abeer Mahmood Kalam, Md Moniruzzaman)

Summary by Cyber Security News15 Oct 2026, 06:00 CEST

About this summary. This is a short, independently written summary of an article first published by arXiv cs.CR. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.19808

arXiv:2610.19808v1 Announce Type: new Abstract: Network Intrusion Detection Systems (NIDS) based on deep learning often fail to detect attack classes that are absent from training data, limiting their robustness against zero-day threats. This property called transferability across attack classes, remains underexplored especially in federated learning (FL) settings where data heterogeneity and client drift pose additional challenges.

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