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Automated cyber threat detection in computer networks is a major challenge incybersecurity. The cyber domain has inherent challenges that make traditionalmachine learning techniques problematic, specifically the need to learncontinually evolving attacks through global collaboration while maintainingdata privacy, and the varying resources available to network owners. We presenta scheme to mitigate these difficulties through an architectural approach usingcommunity model sharing with a streaming analytic pipeline. Our streamingapproach trains models incrementally as each log record is processed, therebyadjusting to concept drift resulting from changing attacks. Further, wedesigned a community sharing approach which federates learning through mergingmodels without the need to share sensitive cyber-log data. Finally, bystandardizing data and Machine Learning processes in a modular way, we providenetwork security operators the ability to manage cyber threat events and modelsensitivity through community member and analytic method weighting in ways thatare best suited for their available resources and data.

Leveraging Sharing Communities to Achieve Federated Learning for Cybersecurity