Understanding Traffic Fingerprinting CNNs

Abstract

HTTPS encrypted traffic can leak information about underlying contents through various statistical properties of traffic flows like packet lengths and timing, opening doors to traffic fingerprinting attacks. Recently proposed traffic fingerprinting attacks leveraged Convolutional Neural Networks (CNNs) and recorded very high accuracies undermining the state-of-the-art mitigation techniques. In this paper, we methodically dissect such CNNs with the objectives of building further accurate and scalable traffic classifiers and understanding the inner workings of such CNNs to develop effective mitigation techniques. By conducting experiments with three datasets, we show that website fingerprinting CNNs focus majorly on the initial parts of traces instead of longer windows of continuous uploads or downloads. Next, we show that traffic fingerprinting CNNs exhibit transfer-learning capabilities allowing identification of new websites with fewer data. Finally, we show that traffic fingerprinting CNNs outperform RNNs because of their resilience to random shifts in data happening due to varying network conditions.