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Adaptive distributed mechanism againts flooding network attacks based on machine learning
dc.contributor.author | Berral García, Josep Lluís |
dc.contributor.author | Poggi Mastrokalo, Nicolas |
dc.contributor.author | Alonso López, Javier |
dc.contributor.author | Gavaldà Mestre, Ricard |
dc.contributor.author | Torres Viñals, Jordi |
dc.contributor.author | Parashar, Manish |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2010-10-26T09:22:45Z |
dc.date.available | 2010-10-26T09:22:45Z |
dc.date.created | 2008 |
dc.date.issued | 2008 |
dc.identifier.citation | Berral, 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. |
dc.identifier.isbn | 978-1-60558-291-7 |
dc.identifier.uri | http://hdl.handle.net/2117/9989 |
dc.description.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. |
dc.format.extent | 7 p. |
dc.language.iso | eng |
dc.publisher | ACM Press, NY |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica |
dc.subject.lcsh | Computer networks -- Security measures |
dc.subject.other | Machine Learning |
dc.subject.other | Flooding Attacks |
dc.subject.other | DDoS |
dc.subject.other | Autonomic Computing |
dc.subject.other | Cooperative |
dc.subject.other | Intrusion Detection |
dc.title | Adaptive distributed mechanism againts flooding network attacks based on machine learning |
dc.type | Conference report |
dc.subject.lemac | Ordinadors, Xarxes d' -- Mesures de seguretat |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.relation.publisherversion | http://portal.acm.org/citation.cfm?id=1456389 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 2396771 |
dc.description.version | Postprint (published version) |
local.citation.author | Berral, J.; Poggi , N.; Alonso, J.; Gavaldà, R.; Torres, J.; Parashar, M. |
local.citation.contributor | ACM Workshop on AISec |
local.citation.pubplace | Alexandria, Virginia |
local.citation.publicationName | The first ACM workshop on AISec |
local.citation.startingPage | 43 |
local.citation.endingPage | 49 |