Name
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). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/NCA.2018.8548317 | 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) |
Keywords | Field | DocType |
deep content,encrypted WiFi traffic,smart devices,digital media consumption,mobile video,content marketing,associated Internet traffic,encryption,user privacy,network surveillance,machine learning techniques,encrypted traffic,end points,video fingerprinting,wireless traffic,WiFi frames,WiFi network,Recurrent Neural Networks,streamed YouTube videos,Convolutional Neural Networks,wired traffic classifier,media access control,multilayer perceptron,video streaming content,network layers | Media access control,Convolutional neural network,Computer science,Recurrent neural network,Computer network,Encryption,Digital media,Perceptron,Internet traffic,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-5386-7660-8 | 1 | 0.36 |
References | Authors | |
5 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Li | 1 | 1 | 0.36 |
| Yi Huang | 2 | 16 | 10.63 |
| Richard Xu | 3 | 4 | 1.42 |
| Suranga Seneviratne | 4 | 155 | 18.43 |
| Kanchana Thilakarathna | 5 | 88 | 15.46 |
| Adriel Cheng | 6 | 1 | 0.36 |
| Darren Webb | 7 | 1 | 0.70 |
| Guillaume Jourjon | 8 | 233 | 33.52 |