SBAR: SDN flow-based monitoring and application recognition

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/122256

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor	Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla
dc.contributor.author	Suárez-Varela Maciá, José Rafael
dc.contributor.author	Barlet Ros, Pere
dc.date	2018
dc.identifier.citation	Suarez-varela, J., Barlet, P. SBAR: SDN flow-based monitoring and application recognition. A: Symposium on SDN Research. "SOSR '18: Symposium on SDN Research: Los Angeles, CA,USA; March 28-29, 2018". New York: Association for Computing Machinery (ACM), 2018.
dc.identifier.citation	978-1-4503-5664-0
dc.identifier.citation	10.1145/3185467.3190788
dc.identifier.uri	http://hdl.handle.net/2117/122256
dc.description.abstract	We present SBAR, a monitoring system compliant with OpenFlow that provides flow-level measurement reports similar to those of NetFlow in traditional networks, but additionally enriched with labels that classify flows at the application layer. For the sake of scalability, we implement flow sampling to control both, the processing overhead in SDN controllers and the memory needed in switches to maintain the flow measurements. Moreover, we leverage the particularities of OpenFlow networks to implement a combination of classification techniques based on DPI and Machine Learning without incurring in high overheads. In particular, we accurately classify the traffic at two different levels: (i) every monitored flow is classified by application protocol, and (ii) for web and encrypted traffic, we apply specific DPI techniques to identify the applications generating each flow. In our demo, we will use real-world traffic to generate flow-level reports with SBAR that are then processed by a commercial monitoring tool to provide a comprehensive high-level view of the traffic in the network [6].
dc.description.abstract	Peer Reviewed
dc.language.iso	eng
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	https://dl.acm.org/citation.cfm?doid=3185467.3190788
dc.relation	info:eu-repo/grantAgreement/AGAUR/PRI2010-2013/2014 SGR 1427
dc.relation	info:eu-repo/grantAgreement/ES/1PE/TEC2014-59583-C2-2-R
dc.relation	info:eu-repo/grantAgreement/AEI/5PN/TEC2017-90034-C2-2-R
dc.relation	info:eu-repo/grantAgreement/EC/H2020/726763
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject	Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject	Computer networks
dc.subject	Machine learning
dc.subject	Openflow
dc.subject	Software-defined networking
dc.subject	Traffic classification
dc.subject	Traffic measurement
dc.subject	Learning systems
dc.subject	Software defined networking
dc.subject	Telecommunication traffic
dc.subject	Application layers
dc.subject	Application protocols
dc.subject	Classification technique
dc.subject	Flow level measurement
dc.subject	Openflow
dc.subject	Processing overhead
dc.subject	Traffic classification
dc.subject	Traffic measurements
dc.subject	Flow measurement
dc.subject	Ordinadors, Xarxes d'
dc.subject	Aprenentatge automàtic
dc.title	SBAR: SDN flow-based monitoring and application recognition
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject

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