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arXiv Machine Learning By Mustafa S. Aljumaily, Ali Ataeemh Allami, Mustafa N. Mnati, Nawar S. Alseelawi, Shahab Abdulla

PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise Networks

Read the original on arXiv Machine Learning →

PVBRF-ID is a two‑server federated learning protocol designed for oilfield enterprise networks that balances privacy and Byzantine robustness. It uses secret‑sharing of model updates, joint norm and cosine‑similarity checks against a clean root set, and cryptographic commitments to make accepted updates publicly verifiable. Experiments on a 15‑class Edge‑IIoT dataset show that PVBRF‑ID matches standard FedAvg on clean data, resists sign‑flip and adaptive poisoning attacks, and protects against update‑inference attacks while still allowing backdoor detection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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