We have developed a novel Outlier Exposure (OE)-enabled cross-silo Federated Learning framework we call FedOE. It empowers the network edge with intelligent DDoS detection by learning from similarities between different data and DDoS attacks available across the edge servers. Our evaluation shows that the novel OE-based Autoencoder we propose achieves a high F1-score for most DDoS attacks, outclassing its non-OE counterpart.
Full abstract available in IEEEXplore DOI 10.1109/TDSC.2022.3224896
Authors
Vahid Pourahmadi – David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada; and Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Hyame Assem Alameddine – Ericsson Security Research, Montreal, Canada.
Mohammad A. Salahuddin – David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada
Raouf Boutaba – David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada
Published in: IEEE Transactions on Dependable and Secure Computing, Volume: 20, Issue: 5, 01, Sept.-Oct. 2023
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