Detecting Colluding Attackers in Distributed Grid Systems

Jan Kantert, Melanie Kauder, Sarah Edenhofer, Sven Tomforde, Christian Müller-Schloer


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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Paper Citation

in Harvard Style

Kantert J., Kauder M., Edenhofer S., Tomforde S. and Müller-Schloer C. (2016). Detecting Colluding Attackers in Distributed Grid Systems . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-172-4, pages 198-206. DOI: 10.5220/0005708301980206

in Bibtex Style

author={Jan Kantert and Melanie Kauder and Sarah Edenhofer and Sven Tomforde and Christian Müller-Schloer},
title={Detecting Colluding Attackers in Distributed Grid Systems},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Detecting Colluding Attackers in Distributed Grid Systems
SN - 978-989-758-172-4
AU - Kantert J.
AU - Kauder M.
AU - Edenhofer S.
AU - Tomforde S.
AU - Müller-Schloer C.
PY - 2016
SP - 198
EP - 206
DO - 10.5220/0005708301980206