Abstract: The classification and identification of Tor and other anonymous traffic is of great significance to network security supervision.However,the existing Tor traffic classification and detection technologies are generally characterized by low identification accuracy,poor real-time performance,and inability to effectively handle high-dimensional data,etc.To solve these problems,a method for online identification of Tor traffic is proposed.A deep neural network based on logistic regression is constructed to extract the matching degree of effective Tor traffic features to implement feature enhancement.Additionally,the artificial bee colony mechanism is used to replace the commonly used iterative algorithms,such as gradient descent,so the traffic classification and recognition results are obtained.The experimental results on public Tor datasets show that compared with the logic regression algorithm,random forest and KNN algorithm,the proposed algorithm improves the accuracy and the recall rate by 10% to 50%.Compared with the regression algorithm based on gradient descent,the proposed algorithm improves the accuracy by 7% to 8%.
Tor traffic identification,
network traffic classification,
network traffic analysis,
artificial bee colony algorithm,