Behavioral Analysis of Traffic Flow for an Effective Network Traffic Identification

Abstract

Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a method to identify network traffics. In this method, for cleaning and preparing data, we perform effective preprocessing approach. Then effective features are extracted using the behavioral analysis of application. Using the effective preprocessing and feature extraction techniques, this method can effectively and accurately identify network traffics. For this purpose, two network traffic databases namely UNIBS and the collected database on router are analyzed. In order to evaluate the results, the accuracy of network traffic identification using proposed method is analyzed using machine learning techniques. Experimental results show that the proposed method obtains an accuracy of 97% in network traffic identification.