Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning
Despite achieving good performance and wide adoption, machine learning based security detection models (e.g., malware classifiers) are subject to concept drift and evasive evolution of attackers, which renders up-to-date threat data as a necessity. However, due to enforcement of various privacy prot...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
07.04.2024
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Subjects | |
Online Access | Get full text |
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