Lightweight Federated Learning Based Detection of Malicious Activity in Distributed Networks
| dc.contributor.author | Wöhnert, Kai Hendrik | |
| dc.contributor.editor | Stolzenburg, Frieder | |
| dc.date.accessioned | 2023-09-20T04:20:43Z | |
| dc.date.available | 2023-09-20T04:20:43Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | 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. | en |
| dc.identifier.doi | 10.18420/ki2023-dc-12 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/42400 | |
| dc.language.iso | en | |
| dc.pubPlace | Bonn | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | DC@KI2023: Proceedings of Doctoral Consortium at KI 2023 | |
| dc.subject | machine learning; malware classification; intrusion detection | en |
| dc.title | Lightweight Federated Learning Based Detection of Malicious Activity in Distributed Networks | en |
| dc.type | Text | |
| gi.citation.endPage | 112 | |
| gi.citation.startPage | 103 | |
| gi.conference.date | 45195 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | Doctoral Consortium at KI 2023 | |
| gi.document.quality | digidoc |
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