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Efficient Distribution-Derived Features for High-Speed Encrypted Flow Classification
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (DISCO)ORCID-id: 0000-0003-3461-7079
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
2018 (Engelska)Ingår i: NetAI'18 Proceedings of the 2018 Workshop on Network Meets AI & ML, New York: Association for Computing Machinery (ACM), 2018, s. 21-27Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Flow classification is an important tool to enable efficient network resource usage, support traffic engineering, and aid QoS mechanisms. As traffic is increasingly becoming encrypted by default, flow classification is turning towards the use of machine learning methods employing features that are also available for encrypted traffic. In this work we evaluate flow features that capture the distributional properties of in-flow per-packet metrics such as packet size and inter-arrival time. The characteristics of such distributions are often captured with general statistical measures such as standard deviation, variance, etc. We instead propose a Kolmogorov-Smirnov discretization (KSD) algorithm to perform histogram bin construction based on the distributional properties observed in the data. This allows for a richer, histogram based, representation which also requires less resources for feature computation than higher order statistical moments. A comprehensive evaluation using synthetic data from Gaussian and Beta mixtures show that the KSD approach provides Jensen-Shannon distance results surpassing those of uniform binning and probabilistic binning. An empirical evaluation using live traffic traces from a cellular network further shows that when coupled with a random forest classifier the KSD-constructed features improve classification performance compared to general statistical features based on higher order moments, or alternative bin placement approaches.

Ort, förlag, år, upplaga, sidor
New York: Association for Computing Machinery (ACM), 2018. s. 21-27
Nyckelord [en]
Traffic classification, Discretization, Machine learning
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-68707DOI: 10.1145/3229543.3229548ISBN: 978-1-4503-5911-5 (digital)OAI: oai:DiVA.org:kau-68707DiVA, id: diva2:1238764
Konferens
2018 Workshop on Network Meets AI & ML. August 24 - 24, 2018. Budapest, Hungary.
Projekt
HITSTillgänglig från: 2018-08-14 Skapad: 2018-08-14 Senast uppdaterad: 2019-11-08Bibliografiskt granskad

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Garcia, JohanKorhonen, Topi

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Totalt: 181 träffar
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