Abstract
With the rapid development of the Internet, different kinds of network games are emerging. The classification of network game flow is important to improve the quality of service. In this paper, we propose an approach to identify network game traffic. It firstly filters the game traffic data based on protocol filtering and IP filtering to reduce background noise as much as possible. Then, to remove irrelevant and redundant features, Pearson correlation coefficient and information gain ratio are used as the criteria to choose features. By analyzing various statistical features of game traffic, it is found that employing the three features, ratio of inbound to outbound data packets, downlink packet size information entropy and downlink Packets per second, is able to yield better classification performance. The experimental results show that the proposed method is feasible and can achieve higher accuracy than an existing method.
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Acknowledgments
The authors would like to thank National Natural Science Foundation of China (No. 61271233) and the HIRP program of Huawei Technology Co. Ltd to sponsor this work in part.
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Dong, Y., Zhang, M., Zhou, R. (2018). Classification of Network Game Traffic Using Machine Learning. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_15
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DOI: https://doi.org/10.1007/978-981-13-0893-2_15
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