Traffic-Based Side-Channel Attack in Video Streaming

Gu, Jiaxi; Wang, Jiliang; Yu, Zhiwen; Shen, Kele

doi:10.1109/tnet.2019.2906568

Cited by 28 publications

(12 citation statements)

References 35 publications

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“…Aceto proposed a novel heuristic to reconstruct application-layer messages from encrypted traffic [18]. Gu et al further utilized DTW to match video fingerprints and traffic patterns, with a classifier to identify encrypted video streams from multiple websites [19]. As the prevalence of deep learning, neural network has an advantage of feature extraction in sophisticated environment.…”

Section: B Privacy Leakage From Video Bitrate Streammentioning

confidence: 99%

“…Distance or similarity measures are essential in solving many pattern recognition problems such as classification and clustering, which are prevalent in signal analysis, speech recognition and other applications [24]. Traditional methods mainly include point based solutions (e.g., Edit Distance on Real Sequence(EDR) [25],Dynamic Time Warping(DTW) Schuster et al [3] a CNN-based model to identify the traffic pattern fingerprints Gu et al [19] a method using DTW to match video fingerprints and traffic patterns Zhang et al [22] a method to identify the zero-day traffic from the tagged traffic using k-means and random forest Hu et al [23] an improved SVM method,which can eliminate the influence of weak correlation and outlier samples…”

Section: Similarity Measurementioning

confidence: 99%

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Encrypted Live Streaming Channel Identification With Time-Sync Comments

et al. 2022

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The time-sync comments have been prevalent in modern live streaming systems to provide a real-time interaction experience for viewers. Whereas, the time-sync comments traffic can also act as a delicate fingerprint of encrypted live channels, leading to potential risks of privacy leakage. Most of previous video channel identification strategies with video bitrate-based fingerprint presume strict requirements on the implementation environments, which often assume that there is no interference from irrelevant traffic flows or network conditions. However, the time-sync comments sessions are distinct from other irrelevant traffic flows, and the traffic pattern is resilient to various network conditions, e.g., bandwidth limitation and transmission delay. In this paper, we design a system for encrypted live channel identification with timesync comments traffic analysis. Specifically, both the inter-application and inner-application traffic filters are proposed to eliminate the irrelevant traffic flows, respectively. Further, a comment rate estimation method is developed through investigation of relationship between comment number, comment length and packet length. Finally, the dynamic time warping algorithm is improved for similarity matching in delay tolerant environment. In order to evaluate the system performance, we setup the prototype system with AWS EC2 server and utilize the real world trace data from Youtube and BiliBili. The experimental results show that the accuracy of the filter can reach 93.2%, and the accuracy of the comment rate estimation method can reach up to 91%. The match accuracy between fingerprint and comment rate can reach 92.1% within 200 seconds eavesdropping, which is 2% higher than using bitrate fingerprint and traffic pattern in the latest research, and can be increased to 98.2% when the eavesdropping time extends to 500 seconds.

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Section: B Privacy Leakage From Video Bitrate Streammentioning

confidence: 99%

Section: Similarity Measurementioning

confidence: 99%

Encrypted Live Streaming Channel Identification With Time-Sync Comments

et al. 2022

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“…Multiple approaches, both algorithmic and machine learning based, have been taken to use this data for malicious purposes. e algorithmic approaches [3,4] seek to measure similarity between the user's bit-rate data and the adversary's prerecorded bitrate data for a specific video. e machine learning approaches [2,5] seek to predict whether or not a user is watching the video selected by the adversary.…”

Section: Traffic Analysis Attackmentioning

confidence: 99%

“…Implementing algorithmic approaches has also been effective in video fingerprinting for identification. Gu et al [3] achieved up to 90% accuracy using a variant of dynamic time warping. Dynamic time warping is an algorithm for comparing time series data.…”

Section: Mpeg-dash Leakmentioning

confidence: 99%

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Efficient yet Robust Privacy Preservation for MPEG-DASH-Based Video Streaming

Cranfill

Kim

Lee

et al. 2021

Security and Communication Networks

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MPEG-DASH is a video streaming standard that outlines protocols for sending audio and video content from a server to a client over HTTP. However, it creates an opportunity for an adversary to invade users’ privacy. While a user is watching a video, information is leaked in the form of meta-data, the size of data and the time the server sent the data to the user. After a fingerprint of this data is created, the adversary can use this to identify whether a target user is watching the corresponding video. Only one defense strategy has been proposed to deal with this problem: differential privacy that adds sufficient noise in order to muddle the attacks. However, that strategy still suffers from the trade-off between privacy and efficiency. This paper proposes a novel defense strategy against the attacks with rigorous privacy and performance goals creating a private, scalable solution. Our algorithm, “No Data are Alone” (NDA), is highly efficient. The experimental results show that our scheme is more than two times efficient in terms of excess downloaded video (represented as waste) compared to the most efficient differential privacy-based scheme. Additionally, no classifier can achieve an accuracy above 7.07% against videos obfuscated with our scheme.

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