Höchst, Jonas ; Hollick, Matthias ; Freisleben, Bernd ; Baumgärtner, Lars (2017)
Unsupervised Traffic Flow Classification Using a Neural Autoencoder.
42nd Annual IEEE Conference on Local Computer Networks (LCN 2017). Singapore, Singapore
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The delay and bandwidth requirements of today's mobile applications differ widely depending on the functionality provided and the data transferred, such as uploading user generated multimedia content, playing online games, or streaming live videos. To perform resource management, mobile wireless networks can particularly profit from classifying and predicting mobile application traffic. State-of-the-art traffic classification approaches have various disadvantages: port-based classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic flow classification using statistical properties of flows and clustering based on a neural autoencoder. In contrast to previous work, the neural autoencoder is used to automatically cluster traffic flows, for example, into downloads, uploads, or voice calls, independent of the particular network protocols, such as FTP or HTTP(S), used for performing these tasks. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic flow classification independent of known traffic classes or classification constraints. An experimental evaluation of the proposed approach on real data captured from about 25 mobile devices performing daily work over a period of 4 months is presented. The obtained results show that 7 different classes of mobile traffic flows are detected with an average precision of 80% and an average recall of 75%, indicating the feasibility of our approach.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Höchst, Jonas ; Hollick, Matthias ; Freisleben, Bernd ; Baumgärtner, Lars |
| Art des Eintrags: | Bibliographie |
| Titel: | Unsupervised Traffic Flow Classification Using a Neural Autoencoder |
| Sprache: | Englisch |
| Publikationsjahr: | 10 Oktober 2017 |
| Ort: | Singapore, Singapore |
| Veranstaltungstitel: | 42nd Annual IEEE Conference on Local Computer Networks (LCN 2017) |
| Veranstaltungsort: | Singapore, Singapore |
| Kurzbeschreibung (Abstract): | The delay and bandwidth requirements of today's mobile applications differ widely depending on the functionality provided and the data transferred, such as uploading user generated multimedia content, playing online games, or streaming live videos. To perform resource management, mobile wireless networks can particularly profit from classifying and predicting mobile application traffic. State-of-the-art traffic classification approaches have various disadvantages: port-based classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic flow classification using statistical properties of flows and clustering based on a neural autoencoder. In contrast to previous work, the neural autoencoder is used to automatically cluster traffic flows, for example, into downloads, uploads, or voice calls, independent of the particular network protocols, such as FTP or HTTP(S), used for performing these tasks. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic flow classification independent of known traffic classes or classification constraints. An experimental evaluation of the proposed approach on real data captured from about 25 mobile devices performing daily work over a period of 4 months is presented. The obtained results show that 7 different classes of mobile traffic flows are detected with an average precision of 80% and an average recall of 75%, indicating the feasibility of our approach. |
| Freie Schlagworte: | Autoencoder; Traffic flow classification; Unsupervised learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Sichere Mobile Netze DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik > Teilprojekt A3: Migration |
| Hinterlegungsdatum: | 19 Okt 2017 12:33 |
| Letzte Änderung: | 10 Jun 2021 06:11 |
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