Internet Traffic Classification Using Score Level Fusion of Multiple Classifier
Masatsugu Ichino, Hiroaki Maeda, Takeshi Yamashita, Kentaro Hoshi, Naohisa Komatsu, Kei Takeshita, Masayuki Tsujino, Motoi Iwashita, Hideaki Yoshino · 2010
Internet traffic is continuously growing fast due to the rapid spread of the internet and the speed-up of the internet connection. Also, the applications provided on the internet have become more diversified. To support the QoS requirements for these internet applications, it would be better to measure the traffic volume according to the applications. Therefore, we are engaged in the application classification method, which is an offline technique for identifying the applications in units of flow. In some application classification methods, the applications of the target flows are analyzed according to their statistics on traffic metric, or features. We focus on these feature based classification methods, since the methods have the advantage that the port number and the packet payload need not be checked for classification. In the field of the machine learning, the classification methods that consist of multiple classifiers have been discussed. This is why the classification methods are improved in performance. However, the conventional feature based classification methods consists of single classifier. Also, the design of multiple classifiers has hardly been discussed. The design includes the way of combining some classifiers. Here, we introduce the fusion of multiple classifiers and propose applying the score level fusion using feature vectors to concatenate each classifier score to classify the flow into applications.