SCNN-Attack: A Side-Channel Attack to Identify YouTube Videos in a VPN and Non-VPN Network Traffic

Khan, Muhammad Usman Shahid; Bukhari, Syed M. A. H.; Maqsood, Tahir; Fayyaz, Muhammad A. B.; Dancey, Darren; Nawaz, Raheel

doi:10.3390/electronics11030350

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…We present a video classification method that uses image processing that has not been discussed in the previous literature. Therefore, we compare our results with the techniques presented in [17,18] and VGG16 [33] for image classification. The entire setup is deployed on Google Colab to train the SCNN model.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Video identification in encrypted Internet traffic has been studied in the last two decades [12,14,24,25,37]. The first and most related to our work that plot the traffic flow as an image and classify among different type of internet traffic is presented by Shapira et al [32].…”

Section: Related Workmentioning

confidence: 99%

“…Examples of the TPP are shown in Figure 1. We train a sequential convolutional neural network (SCNN), as presented in [17,18] on our datasets. The model diagram is presented in Figure 2.…”

Section: Traffic Pattern Plotmentioning

confidence: 99%

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Traffic Pattern Plot: Video Identification in Encrypted Network Traffic

Kamal

Bukhari

Khan

et al. 2023

Lecture Notes in Networks and Systems

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Most of the internet traffic is encrypted and it is a challenge to identify streaming videos in the internet traffic. In this paper, we present a methodology named Traffic Pattern Plot (TPP) to identify video streams in encrypted network traffic. The proposed methodology plots the video traffic flows and uses a Convolutional Neural Network (CNN) to detect the videos. The results show that the traffic pattern plot generated from 120 seconds of sniffing network traffic is enough to identify the video even in the encrypted network traffic with 94% accuracy.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Traffic Pattern Plot: Video Identification in Encrypted Network Traffic

Kamal

Bukhari

Khan

et al. 2023

Lecture Notes in Networks and Systems

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“…BPS is an important feature that plays an essential role in video identification. Khan et al [21], [46] extracted the BPS of a video stream multiple times in different video qualities and used them as a feature. This feature is used to train different machine learning models, including Naïve Bayes, SVMs (Support Vector Machines), and CNN.…”

Section: Related Workmentioning

confidence: 99%

Fingerprinting Technique for YouTube Videos Identification in Network Traffic

et al. 2022

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Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users' activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) -the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic.

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