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Lightweight Federated Learning Based Detection of Malicious Activity in Distributed Networks

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2023

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Gesellschaft für Informatik e.V.

Zusammenfassung

In an increasingly complex cyber threat landscape, traditional malware detection methods often fall short, particularly within resource-limited distributed networks like smart grids. This research project aims to develop an efficient malware detection system for such distributed networks, focusing on three elements: feature extraction, feature selection, and classification. For classification, a lightweight and accurate machine-learning model needs to be developed.

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Wöhnert, Kai Hendrik (2023): Lightweight Federated Learning Based Detection of Malicious Activity in Distributed Networks. DC@KI2023: Proceedings of Doctoral Consortium at KI 2023. DOI: 10.18420/ki2023-dc-12. Gesellschaft für Informatik e.V.. pp. 103-112. Doctoral Consortium at KI 2023. Berlin. 45195

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