scholarly journals Preventing the Video Leakages from The Traffic Streaming

2020 ◽  
pp. 509-516
Author(s):  
Arunapriya R
Keyword(s):  
Video Streaming ◽  
Network Traffic ◽  
Identification Accuracy ◽  
Adaptive Streaming ◽  
Video Content ◽  
Variable Bit Rate ◽  
Bit Rate ◽  
Feature Extraction Method ◽  
Video Feature ◽  
Streaming Traffic

Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic Adaptive Streaming over HTTP (DASH) becomes the defacto standard of video streaming and it is adopted by YouTube, Netflix, etc.Despite of the popularity, network traffic during video streaming shows identifiable pattern which brings threat to user privacy.In this paper, to proposea video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bit rate trend remains relatively stable because of the widely used Variable Bit-Rate(VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, to propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. To evaluate our attack method in different scenarios for various video content, segment lengths and quality levels. The experimental results show that the identification accuracy can reach up to 90%using only three minute continuous network traffic eavesdropping.

scholarly journals Video Streaming Service Identification on Software-Defined Networking

2021 ◽  
Vol 16 (5) ◽  
Author(s):  
Luis Miguel Castañeda Herrera ◽  
Wilmar Yesid Campo-Muñoz ◽  
Alejandra Duque Torres
Keyword(s):  
Video Streaming ◽  
Network Traffic ◽  
Network Performance ◽  
Video On Demand ◽  
Traffic Classification ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
Video Traffic ◽  
Tree Model ◽  
Streaming Traffic

It is well known that video streaming is the major network traffic today. Futhermore, the traffic generated by video streaming is expected to increase exponentially. On the other hand, SoftwareDefined Networking (SDN) has been considered a viable solution to cope with the complexity and increasing network traffic due to its centralised control and programmability features. These features, however, do not guarantee that network performance will not suffer as traffic grows. As result, understanding video traffic and optimising video traffic can aid in control various aspects of network performance, such as bandwidth utilisation, dynamic routing, and Quality of Service (QoS). This paper presents an approach to identify video streaming traffic in SDN and investigates the feasibility of using Knowledge-Defined Networking (KDN) in traffic classification. KDN is a networking paradigm that takes advantage of Artificial Intelligence (AI) by using Machine Learning approaches, which allows integrating behavioural models to detect patterns, like video streaming traffic identification, in SDN traffic. In our initial proof-of-concept, we derive the relevant information of network traffic in the form of flows statistics. Then, we used such information to train six ML models that can classify network traffic into three types, Video on Demand (VoD), Livestream, and no-video traffic. Our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy using the Decision Tree model.

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