A Simplified Machine Learning Approach to Classifying Individual Websites

We quantify the classification accuracy of Neural Networks (NNs) to specific websites using only the packet size and difference in inter-packet arrival time, which are easily observable via passive attackers in the network. Our flow classification work with NNs is unique in that we do not classify traffic by application type. Rather, we observe the accuracy of various NNs classifying specific web sites using HTTP traffic over TCP. We test a diverse set of neural network structures including a fully connected network (FCN), a convolutional neural network (CNN), a long short-term memory network (LSTM), and an autoencoder network (AE). We found that CNNs consistently had the highest accuracy, typically 80-90% when using 20 million packets as training data. We suspect that individual websites generate unique traffic patterns which are discoverable using NN techniques. Our work has important privacy implications. In particular, our work supports that both packet sizes and inter-packet timing must be randomized to obtain strong web browsing privacy. Many privacy preserving techniques, such as VPNs, will require additional enhancements.