Evaluation of HTTP video classification method using flow group information
Kei Takeshita, Takeshi Kurosawa, Masayuki Tsujino, Motoi Iwashita, Masatsugu Ichino, Naohisa Komatsu · 2010
Recently, the traffic volume of HTTP video applications, such as YouTube, is rapidly growing on the Internet. To support the quality of service requirements of HTTP video applications, network carriers need to design bandwidth by taking into account the traffic volume of HTTP video applications. However, since most HTTP video applications are provided in a web browser, it is difficult to classify HTTP video applications with other web applications by the port number. We propose an HTTP video application classification method by using a machine learning method with traffic-flow features such as packet size. We propose a new feature that is useful in classifying HTTP video applications. We can improve the accuracy of traffic classification of HTTP video applications by 12%. Furthermore, we compare the accuracy and calculation time among three machine learning methods.