Özkan, Hüseyin and Temelli, Recep and Gürbüz, Özgür and Köksal, Oğuz Kaan and İpekören, Ahmet Kaan and Canbal, Furkan and Karahan, Baran Deniz and Kuran, Mehmet Şükrü (2021) Multimedia traffic classification with mixture of Markov components. Ad Hoc Networks, 121 . ISSN 1570-8705 (Print) 1570-8713 (Online) Published Online First http://dx.doi.org/10.1016/j.adhoc.2021.102608
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Official URL: http://dx.doi.org/10.1016/j.adhoc.2021.102608
Abstract
We study multimedia traffic classification into popular applications to assist the quality of service (QoS) support
of networking technologies, including but not limited to, WiFi. For this purpose, we propose to model the
multimedia traffic flow as a stochastic discrete-time Markov chain in order to take into account the strong
sequentiality (i.e. the dependencies across the data instances) in the traffic flow observations. This addresses
the shortcoming of the prior techniques that are based on feature extraction which is prone to losing the
information of sequentiality. Also, for investigating the best application of our Markov approach to traffic
classification, we introduce and test three data driven classification schemes which are all derived from the
proposed model and tightly related to each other. Our first classifier has a global perspective of the traffic data
via the likelihood function as a mixture of Markov components (MMC). Our second and third classifiers have
local perspective based on k-nearest Markov components (kNMC) with the negative loglikelihood as a distance
as well as k-nearest Markov parameters (kNMP) with the Euclidean distance. We additionally introduce to
the use of researchers a rich multimedia traffic dataset consisting of four application categories, e.g., video
on demand, with seven applications, e.g., YouTube. In the presented comprehensive experiments with the
introduced dataset, our local Markovian approach kNMC outperforms MMC and kNMP and provides excellent
classification performance, 89% accuracy at the category level and 85% accuracy at the application level and
particularly over 95% accuracy for live video streaming. Thus, in test time, the nearest Markov components
with the largest likelihoods yield the most discrimination power. We also observe that kNMC significantly
outperforms the state-of-the-art methods (such as SVM, random forest and autoencoder) on both the introduced
dataset and benchmark dataset both at the category and application levels
Item Type: | Article |
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Uncontrolled Keywords: | Multimedia, Traffic classification, Machine learning, Markov models, Wireless last-hop, WiFi |
Divisions: | Faculty of Engineering and Natural Sciences > Academic programs > Electronics Faculty of Engineering and Natural Sciences |
Depositing User: | Hüseyin Özkan |
Date Deposited: | 21 Aug 2021 16:05 |
Last Modified: | 21 Aug 2021 16:05 |
URI: | https://research.sabanciuniv.edu/id/eprint/41910 |
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