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Detecting colluding attackers in distributed grid systems

  • Distributed grid systems offer possible benefits in terms of fast computation of tasks. This is accompanied by potential drawbacks due to their openness, the heterogeneity of participants, and the unpredictability of agent behaviour, since agents have to be considered as black-boxes. The utilisation of technical trust within adaptive collaboration strategies has been shown to counter negative effects caused by these characteristics. A major challenge in this context is the presence of colluding attackers that try to exploit or damage the system in a coordinated fashion. Therefore, this paper presents a novel approach to detect and isolate such colluding attackers. The concept is based on observations of interaction patterns and derives a classification of agent communities. Within the evaluation, we demonstrate the benefit of the approach and highlight the highly reliable classification.

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Metadaten
Author:Jan Kantert, Melanie Kauder, Sarah Edenhofer, Sven TomfordeGND, Christian Müller-Schloer
URN:urn:nbn:de:bvb:384-opus4-1200412
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/120041
ISBN:978-989-758-172-4OPAC
Parent Title (English):Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016), February 24-26, 2016, Rome, Italy, volume 1
Publisher:SciTePress
Place of publication:Setúbal
Editor:Jaap van den Herik, Joaquim Filipe
Type:Conference Proceeding
Language:English
Year of first Publication:2016
Publishing Institution:Universität Augsburg
Release Date:2025/03/12
First Page:198
Last Page:206
DOI:https://doi.org/10.5220/0005708301980206
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung

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