The Community for Technology Leaders
2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) (2018)
Cambridge, MA, USA
Nov. 1, 2018 to Nov. 3, 2018
ISBN: 978-1-5386-7660-8
pp: 1-8
Ying Li , University Technology of Sydney, Australia
Yi Huang , University Technology of Sydney, Australia
Richard Xu , University Technology of Sydney, Australia
Suranga Seneviratne , University of Sydney, Australia
Kanchana Thilakarathna , University of Sydney, Australia
Adriel Cheng , Defence Science & Technology Group, Edinburgh, Australia
Darren Webb , Defence Science & Technology Group, Edinburgh, Australia
Guillaume Jourjon , Data61-CSIRO, Sydney, Australia
ABSTRACT
The proliferation of smart devices has led to an exponential growth in digital media consumption, especially mobile video for content marketing. The vast majority of the associated Internet traffic is now end-to-end encrypted, and while encryption provides better user privacy and security, it has made network surveillance an impossible task. The result is an unchecked environment for exploiters and attackers to distribute content such as fake, radical and propaganda videos. Recent advances in machine learning techniques have shown great promise in characterising encrypted traffic captured at the end points. However, video fingerprinting from passively listening to encrypted traffic, especially wireless traffic, has been reported as a challenging task due to the difficulty in distinguishing retransmissions and multiple flows on the same link. We show the potential of fingerprinting videos by passively sniffing WiFi frames in air, even without connecting to the WiFi network. We have developed Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) that are able to identify streamed YouTube videos from a closed set, by sniffing WiFi traffic encrypted at both Media Access Control (MAC) and Network layers. We compare these models to the state-of-the-art wired traffic classifier based on Convolutional Neural Networks (CNNs), and show that our models obtain similar results while requiring significantly less computational power and time (approximately a threefold reduction).
INDEX TERMS
Streaming media, Wireless fidelity, Internet, Protocols, Encryption
CITATION

Y. Li et al., "Deep Content: Unveiling Video Streaming Content from Encrypted WiFi Traffic," 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA, 2018, pp. 1-8.
doi:10.1109/NCA.2018.8548317
265 ms
(Ver 3.3 (11022016))