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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/89732


    Title: 在TLS交握過程中使用SNI辨識影音串流平台服務;Identifying Public Video Streaming data based on SNI in TLS handshaking
    Authors: 黃彥愷;Huang, Yen-Kia
    Contributors: 資訊工程學系
    Keywords: 影音串流平台分析與辨識;網路安全;OpenAppID;snort
    Date: 2022-07-07
    Issue Date: 2022-10-04 11:57:45 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 本論文參考客戶端與各影音傳輸平台的CDN之間的連接,並且考慮各影音傳輸平台的CDN傳輸架構,提出利用TCP連接的SNI 辨識出影音傳輸平台的影音流量,根據cisco VNI指出,這些影音傳輸平台在網路總流量中占很大一部分,辨識出影音流量,可以幫助網路管理者管理網路流量,可以幫助網路管理者在特定時間封鎖這些平台使網路流量有更好的負載平衡,或是可以使這些影音流量通過,讓網路管理者可以專注於找出惡意流量。
    本研究提出辨識常使用的影音串流平台:Netflix、YouTube、愛奇藝(iQiyi)、HBO:GO的方法,針對real-time的資料記錄下來分析,且達到高度準確率。希望可以藉由此分析方法,幫助網路管理者過濾、阻擋、分析公司內部的網路流量管理及平衡網路負載,以達成最佳網路資源使用率、降低網路流量成本,且可幫助網路管理員降低分析網路流量的複雜度。
    ;This paper considers the connection between the client and the CDN of video streaming provider platform, and the CDN transmission architecture of each video streaming provider, and proposes to identify video streaming traffic by using the SNI of the TCP connection. According to cisco VNI, these video streaming platforms account for a large part of the total network traffic. Identifying these video streaming traffic can help network managers manage network traffic. It can help network managers block these platforms at specific times to balance loading
    of network traffic, or being able to pass these video streaming traffic through, allows network managers to focus on finding malicious traffic.
    This study proposes a method to identify commonly used video streaming platforms, like: Netflix, YouTube, iQiyi, HBO:GO, record and analyze real-time data, and achieve high accuracy. It is hoped that this analysis method can help network managers to filter, block, and analyze network traffic and balance network loading within the company, so as to achieve optimal network resource utilization, reduce network traffic costs, and help Network managers reduce the complexity of analyzing network traffic.
    Appears in Collections:[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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