PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise Networks
Federated learning allows dispersed oilfield sites to train a shared intrusion detector without pooling network traffic, yet its aggregation step forces a choice between privacy and robustness.
ProofPaper ↗
Key points
- Byzantine-robust rules must read each update in the clear, whereas secure aggregation hides updates from the checks that detect poisoning.
- We present PVBRF-ID, a two-server protocol in which sites secret-share their updates and the servers jointly test each update's norm and its cosine similarity to a reference computed on a small clean root set, revealing one accept bit per site.
- On a 15-class Edge-IIoTset task with ten sites holding differently distributed data and ten random seeds, PVBRF-ID matched FedAvg on clean data (macro-F1 0.708 vs. 0.706) and withstood sign-flip attacks (0.696 vs. 0.091).
- An update-inference attacker recovered each site's class mix from plaintext updates (Spearman correlation 0.81) but not from one server's view.
Sources (1)
- [1]PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise NetworksarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:34 PM
Federated learning allows dispersed oilfield sites to train a shared intrusion detector without pooling network traffic, yet its aggregation step forces a choice between privacy and robustness.
Byzantine-robust rules must read each update in the clear, whereas secure aggregation hides updates from the checks that detect poisoning.
Extractive summary: sentences quoted from the sources.