Using of Time Characteristics in Data Flow for Traffic Classification

Abstract : This paper describes a protocol detection using statistic information about a flow extended by packet sizes and time characteristics, which consist of packet inter-arrival times. The most common way of network traffic classification is a deep packet inspection (DPI). Our approach deals with the DPI disadvantage in power consumption using aggregated IPFIX data instead of looking into packet content. According to our previous experiments, we have found that applications have their own behavioral pattern, which can be used for the applications detection. With a respect to current state of development, we mainly present the idea, the results which we have achieved so far and of our future work.
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Isabelle Chrisment; Alva Couch; Rémi Badonnel; Martin Waldburger. 5th Autonomous Infrastructure, Management and Security (AIMS), Jun 2011, Nancy, France. Springer, Lecture Notes in Computer Science, LNCS-6734, pp.173-176, 2011, Managing the Dynamics of Networks and Services. 〈10.1007/978-3-642-21484-4_21〉
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Pavel Piskac, Jiri Novotny. Using of Time Characteristics in Data Flow for Traffic Classification. Isabelle Chrisment; Alva Couch; Rémi Badonnel; Martin Waldburger. 5th Autonomous Infrastructure, Management and Security (AIMS), Jun 2011, Nancy, France. Springer, Lecture Notes in Computer Science, LNCS-6734, pp.173-176, 2011, Managing the Dynamics of Networks and Services. 〈10.1007/978-3-642-21484-4_21〉. 〈hal-01585856〉

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