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                            Library & Multimedia
 VideoTrain plus plus: GAN-based adaptive framework for synthetic video traffic generation
NCJ Number
              307357

Author(s)
          Chamara Madarasingha;
                        Shashika R. Muramudalige;
                        Guillaume Jourjon;
                        Anura Jayasumana;
                        Kanchana Thilakarathna
Date Published

2022
Annotation
              The authors describe the need for, and their development of, a novel Generative Adversarial Network-based data generation solution for network security and privacy; the solution consists of a percentile-based data mapping mechanism to enhance the data generation process, and is designed to synthesize video streaming data for 360°/normal video classification and video fingerprinting.
Abstract
              Video streaming traffic has been dominating the global network and the challenges have exacerbated with the gaining popularity of interactive videos, also known as 360° videos, as they require more network resources. However, effective provision of network resources for video streaming traffic is problematic due to the inability to identify video traffic flows through the network because of end-to-end encryption. Despite the promise given for network security and privacy, end-to-end encryption also provides a shield for adversaries. To this end, encrypted traffic classification and content fingerprinting with advanced Machine Learning (ML) methods have been proposed. Nevertheless, achieving high performance requires a significant amount of training data, which is a challenging task in operational networks due to the sheer volume of traffic and privacy concerns. As a solution, in this paper, the authors propose a novel Generative Adversarial Network (GAN)-based data generation solution to synthesize video streaming data for two different tasks, 360°/normal video classification and video fingerprinting. The solution consists of a percentile-based data mapping mechanism to enhance the data generation process, which is further supported by novel algorithms for data pre-processing and GAN model training. Taking over 6,600 actual video traces and generating over 150,000 new traces, our ML-based traffic classification results show a five- to 16-percent of accuracy improvement in both tasks. Publisher Abstract Provided
      
  
          Additional Details
Grant Number(s)
        2017-ZA-CX-0002


  
  
        
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        NIJ-Sponsored


  
  
        
  Publication Type
        Report (Study/Research)


  
  
        
  Award
        Dynamic, Graph-Based Risk Assessments for the Detection of Violent Extremist Radicalization Trajectories Using Large Scale Social and Behavioral Data
    
  
  
Date Published: January 1, 2022
    
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