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Adaptive distributed mechanism againts flooding network attacks based on machine learning

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hdl:2117/9989

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Berral García, Josep LluísMés informacióMés informacióMés informació
Poggi Mastrokalo, Nicolas
Alonso López, Javier
Gavaldà Mestre, RicardMés informacióMés informació
Torres Viñals, JordiMés informacióMés informacióMés informació
Parashar, Manish
Document typeConference report
Defense date2008
PublisherACM Press, NY
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Adaptive techniques based on machine learning and data mining are gaining relevance in self-management and self- defense for networks and distributed systems. In this paper, we focus on early detection and stopping of distributed flooding attacks and network abuses. We extend the framework proposed by Zhang and Parashar (2006) to cooperatively detect and react to abnormal behaviors before the target machine collapses and network performance degrades. In this framework, nodes in an intermediate network share infor- mation about their local traffic observations, improving their global traffic perspective. In our proposal, we add to each node the ability of learning independently, therefore reacting differently according to its situation in the network and local traffic conditions. In particular, this frees the administrator from having to guess and manually set the parameters distinguishing attacks from non-attacks: now such thresholds are learned and set from experience or past data. We expect that our framework provides a faster detection and more accuracy in front of distributed ooding attacks than if static filters or single-machine adaptive mechanisms are used. We show simulations where indeed we observe a high rate of stopped attacks with minimum disturbance to the legitimate users.
CitationBerral, J. [et al.]. Adaptive distributed mechanism againts flooding network attacks based on machine learning. A: ACM Workshop on AISec. "The first ACM workshop on AISec". Alexandria, Virginia: ACM Press, NY, 2008, p. 43-49. 
URIhttp://hdl.handle.net/2117/9989
ISBN978-1-60558-291-7
Publisher versionhttp://portal.acm.org/citation.cfm?id=1456389
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  • Departament de Ciències de la Computació - Ponències/Comunicacions de congressos [1.249]
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  • LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge - Ponències/Comunicacions de congressos [119]
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