Internet Traffic Classification Algorithm Based on Hybrid Classifiers to Identify Online Games Traffic
DOI:
https://doi.org/10.11113/jt.v64.2079Keywords:
Internet traffic classification, online games, online classification, machine learning, classification algorithmAbstract
Classification of interactive applications such as online games has gained more attention in the last few years. However, most of the current classification methods were only valid for offline classification. The three common classification methods i.e. port, payload and statistics based have some limitations. This paper exploits the advantages of all the three methods by combining them to produce a new classification algorithm called SSPC (Signature Static Port Classifier). In the proposed algorithm, each of the three classifiers will individually classify the same traffic flow. Based on some priority rules, SSPC makes classification decision for each flow. The SSPC algorithm was used to classify online game (LOL) traffic in two stages, initially offline and later online. SSPC produces a higher accuracy of 91% on average for online classification when compared with other classifiers. In addition, as demonstrated in the real time online experiments done, SSPC algorithm uses a short time to classify traffic and thus it is suitable to be used for online classification.
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