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Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
2018
Applied Sciences
The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by
doi:10.3390/app8122663
fatcat:22w3om3rwnbgjd7nuo3kdbjn3i