An analysis of Network Traffic Identification based on Decision Tree

With the strong development momentum of the Internet, network not only brings convenience to our life, but also poses threats to our network security. Users can hide their IP and identity information through encryption algorithm, and carry out various illegal activities and transactions in the network. Therefore, it will be of great value to identify special traffic which impact on network security and exploring abnormal situations in the network. Special traffic is important to the security of the network space. In this paper, we establish a decision tree model based on C4.5 and random forest to compare the accuracy of the two decision tree algorithms in traffic classification. Experimental results show that these two methods can identify traffic effectively.