基於Snort實作網路影音串流服務平台之辨識與過濾機制;Identification and Filtering mechanism of video streaming service platform based on Snort

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Title:	基於Snort實作網路影音串流服務平台之辨識與過濾機制;Identification and Filtering mechanism of video streaming service platform based on Snort
Authors:	郭庭余;Kuo, Ting-Yu
Contributors:	資訊工程學系
Keywords:	影音串流服務分析與辨識;Snort;網路流量分析;網路安全;Video Streaming Service Analysis and Identification;Snort;Network Traffic Analysis;Network security
Date:	2021-08-20
Issue Date:	2021-12-07 13:09:32 (UTC+8)
Publisher:	國立中央大學
Abstract:	根據Cisco報告顯示，影音串流服務在網路流量中佔有很大比例，並且還在快速增長，報告中預測影音串流服務將在2021年佔據所有消費者網路流量的82%。根據Sandvine在2020年的分析，Netflix佔據全球應用共享流量的11.42%，YouTube則佔據15%，光是這兩大影音串流平台就佔據全球應用流量的26.42%。由此可知影音串流服務雖然是單一類別，但網路流量很大、占用大量頻寬，因此如何辨識影音串流服務顯得格外重要。如果有方法能夠辨識影音串流服務，則可先將影音串流服務的網路流量進行過濾或直接阻擋，藉由過濾或阻擋影音串流服務的網路流量，可以降低網路管理員分析其他服務網路流量的複雜度。本研究提出辨識兩大網路影音串流服務平台Netflix與YouTube的方法，針對這兩大平台的offline分析或real-time分析皆達到98%準確率與100%精確率，不會將非Netflix或非YouTube流量誤攔。希望以此協助公司內部的網路管理員過濾網路流量和平衡網路負載，以達成提高網路資源有效利用率、降低網路使用成本、公司內部網路安全等考量。;According to a Cisco report, video streaming services account for a large proportion of network traffic and are still growing rapidly. The report predicts that video streaming services will account for 82% of all consumer Internet traffic in 2021. According to Sandvine′s analysis in 2020, Netflix accounted for 11.42% of global application traffic sharing, while YouTube accounted for 15%. The two leading video streaming service platforms alone accounted for 26.42% of global application traffic. Video streaming services are a single category, but the network traffic is large and takes up a lot of bandwidth. Therefore, how to identify video streaming services is extremely important. If there is a method to identify the video streaming service, the network traffic of the video streaming service can be filtered or directly blocked. By filtering or blocking the network traffic of the video streaming service, it can reduce the complexity of network administrators analyzing the network traffic of other services. This research proposes a method to identify the two major online video streaming service platforms Netflix and YouTube. Both offline analyze or real-time analyze of these two major online video streaming service platforms have achieved 98% accuracy and 100% precision. Hope to assist the network administrators of the company to filter network traffic and balance the network load, so as to improve the effective utilization of network resources, reduce the cost of network usage, and consider the internal network security of the company.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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