Coordinated team learning and difference rewards for distributed intrusion response

Kleanthis Malialis, Sam Devlin, Daniel Kudenko

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Distributed denial of service attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to respond to such attacks. We demonstrate that our approach can significantly scale-up using hierarchical communication and coordinated team learning. Furthermore, we incorporate a form of reward shaping called difference rewards and show that the scalability of our system is significantly improved in experiments involving over 100 reinforcement learning agents. We also demonstrate that difference rewards constitute an ideal online learning mechanism for network intrusion response. We compare our proposed approach against a popular state-of-the-art router throttling technique from the network security literature, and we show that our proposed approach significantly outperforms it. We note that our approach can be useful in other related multiagent domains.

Original languageEnglish
Title of host publicationECAI 2014
Subtitle of host publication21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
PublisherIOS Press
Number of pages2
ISBN (Electronic)9781614994183
Publication statusPublished - 2014
Event21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic
Duration: 18 Aug 201422 Aug 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)09226389


Conference21st European Conference on Artificial Intelligence, ECAI 2014
Country/TerritoryCzech Republic

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