Temporal Sequence of Data Fluctuation-Based Approach for Tor Program Classification

With the continuous development of encryption technology, the share of encrypted traffic in the network is increasing, which brings great challenges to the traditional methods of rule-based traffic identification. Deep learning is becoming an inspiring methodology to solve the problem. Previous studies have confirmed that time characteristics play an important role in Tor traffic classification. We find that there is a similarity of time characteristics among different programs. This paper proposes an end-to-end classification framework: the temporal sequence of data fluctuation network (TSDFN). It first extracts the temporal sequence of data fluctuation in the original flow and then uses the GRU network to learn the hidden temporal features. Experiments on public data sets validate the effectiveness of our proposal over other methods.