Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77335
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorGangadhar, S-
dc.creatorSterbenz, JPG-
dc.date.accessioned2018-07-30T08:27:38Z-
dc.date.available2018-07-30T08:27:38Z-
dc.identifier.isbn9781538606711-
dc.identifier.urihttp://hdl.handle.net/10397/77335-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectDoS attacken_US
dc.subjectFuture Interneten_US
dc.subjectMachine learningen_US
dc.subjectNetwork securityen_US
dc.subjectOpenFlowen_US
dc.subjectResilienceen_US
dc.subjectSDNen_US
dc.subjectSurvivabilityen_US
dc.subjectSYN flooden_US
dc.subjectTraffic toleranceen_US
dc.titleMachine learning aided traffic tolerance to improve resilience for software defined networksen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1109/RNDM.2017.8093035-
dcterms.abstractSoftware Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.-
dcterms.bibliographicCitationProceedings of 2017 9th International Workshop on Resilient Networks Design and Modeling, RNDM 2017, 4-6 Sept 2017, 8093035-
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85040508111-
dc.relation.conferenceInternational Workshop on Resilient Networks Design and Modeling [RNDM]-
dc.identifier.artn8093035-
dc.description.validate201807 bcrc-
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