Implementation of Distance Based Semi Supervised Clustering and Probabilistic Assignment Technique for Network Traffic Classification

Network Traffic Classification is an important process in various network management activities like network planning, designing, workload characterization etc. Network traffic classification using traditional techniques such as well known port number based and payload analysis based techniques are no more effective because various applications uses port hopping and encryption technique to avoid detection. Recently machine learning techniques such as supervised, unsupervised and semi supervised techniques are used to overcome the problems of traditional techniques. In this work we use semi supervised machine learning approach and proposed distance based semi supervised clustering and probabilistic assignment technique for network traffic classification. This technique used only flow statistics to classify network traffic. It permits to build the classifier using both labeled and unlabeled instances in training dataset.