Towards Fast Network Intrusion Detection based on Efficiency-preserving Federated Learning

2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)(2021)

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摘要
Network Intrusion Detection Systems (NIDSs) are extremely important in defending against emergent cyberattacks. However, current NIDSs for Internet-of-Things (IoT) devices have not taken actual device computation limitation into account, and are still based on resource-consuming neural networks. In this paper, we propose a simple but effective FL-based NIDS. Specifically, we leverage the character...
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关键词
Intrusion Detection,Communication-efficient,Privacy-preserving,Federated Learning,Internet-of-Things
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