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An Architecture for Federated Learning Enabled Collaborative Intrusion Detection System
2021
Americas Conference on Information Systems
Intrusion Detection Systems (IDSs) are essential part of a security solution to monitor the network traffics and detect malicious attacks. In a collaborative IDS, multiple IDSs work together to effectively detect large scaled and across platforms or operating systems attacks. However, the participating nodes in a CIDS may hesitate to share their network traffic data due to privacy concerns. In this paper, we propose a federated learning enabled CIDS architecture leveraging its privacy-reserving
dblp:conf/amcis/McOskerHLSZ21
fatcat:x7trary3p5gcfn4az5u2y2fuy4