Evaluating machine learning methods for online game traffic identification

Online gaming is becoming more and more prominent in the Internet, in terms of both traffic volume and as a potential source of revenue. Quality of Service (QoS) requirements for highly interactive games are much stricter than for traditional Internet applications, such as web or email. For effective QoS implementations that are transparent to users and game applications, an accurate and reliable method of classifying game traffic flows in the network must be found. Current methods such as port number and payload-based identification exhibit a number of shortfalls. A potential solution is the use of Machine Learning techniques to identify game traffic based on payload independent statistical features such as packet length distributions. In this paper we evaluate the effectiveness of the proposed approach. We compare the accuracy and performance of different Machine Learning techniques and we also use feature selection techniques to examine which features are most important in discriminating game traffic from other traffic. We find that machine learning algorithms are able to separate online game traffic from other network traffic with very high (>99%) accuracy. We also show that feature selection, while reducing accuracy, allows games to be identified with fewer features and substantial speed gains