Title :
Internet Traffic Classification Using Score Level Fusion of Multiple Classifier
Author :
Ichino, Masatsugu ; Maeda, Hiroaki ; Yamashita, Takeshi ; Hoshi, Kentaro ; Komatsu, Naohisa ; Takeshita, Kei ; Tsujino, Masayuki ; Iwashita, Motoi ; Yoshino, Hideaki
Author_Institution :
Media Network Center, Waseda Univ., Tokyo, Japan
Abstract :
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.
Keywords :
Internet; learning (artificial intelligence); quality of service; telecommunication traffic; Internet traffic classification; QoS requirements; internet applications; internet connection; machine learning; multiple classifier; score level fusion; Accuracy; Internet; Machine learning; Payloads; Postal services; Support vector machine classification; Training; application classification; internet traffic; multiple classifier; score level fusion;
Conference_Titel :
Computer and Information Science (ICIS), 2010 IEEE/ACIS 9th International Conference on
Conference_Location :
Yamagata
Print_ISBN :
978-1-4244-8198-9
DOI :
10.1109/ICIS.2010.48