基於Snort實作網路影音串流服務平台之辨識與過濾機制

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郭庭余(Ting-Yu Kuo) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

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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摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 影音串流服務分析與辨識
★ Snort
★ 網路流量分析
★ 網路安全

關鍵字(英)

★ Video Streaming Service Analysis and Identification
★ Snort
★ Network Traffic Analysis
★ Network security

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 viii
一、緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 論文架構 2
二、背景知識 3
2-1 資訊安全防禦基礎知識 3
2-1-1 網路模型 3
2-1-2 網路安全解決方案 6
2-2 Snort 8
2-2-1 Snort簡介 8
2-2-2 Snort架構 10
2-2-3 OpenAppID 13
2-2-4 Snort Rule 14
三、相關研究 15
四、系統設計 16
4-1 系統架構 16
4-2 Netflix辨識 21
4-2-1 收集Netflix IP 22
4-2-2 Netflix OpenAppID 23
4-2-3 更新Netflix Snort Rules 24
4-2-4 Offline辨識Netflix 24
4-2-5 Real-Time辨識Netflix 25
4-3 YouTube辨識 26
4-3-1 YouTube IP收集 27
4-3-2 YouTube OpenAppID 28
4-3-3 更新YouTube Snort Rules 29
4-3-4 Offline辨識YouTube 29
4-3-5 Real-Time辨識YouTube 30
4-4 Netflix與YouTube同時辨識 31
4-4-1 Offline辨識Netflix與YouTube 31
4-4-2 Real-Time辨識Netflix與YouTube 32
五、實驗結果 33
5-1 實驗環境 33
5-2 Netflix辨識 34
5-2-1 Offline辨識Netflix 34
5-2-2 Real-time辨識Netflix 36
5-3 YouTube辨識 38
5-3-1 Offline辨識YouTube 38
5-3-2 Real-time辨識YouTube 40
5-4 Netflix與YouTube同時辨識 42
5-4-1 Offline辨識Netflix與YouTube 42
5-4-2 Real-time辨識Netflix與YouTube 44
5-5 Snort Rule數量對於Snort分析效率的討論 46
六、結論與未來方向 48
參考資料 49

參考文獻

[1] C. V. networking Index, “Forecast and methodology, 2016-2021, white paper,” San Jose, CA, USA, vol. 1, 2016.
[2] “Global Internet Phenomena Report.” https://www.sandvine.com/phenomena (accessed Mar. 17, 2021)
[3] Roesch, Martin. "Snort: Lightweight intrusion detection for networks." Lisa. Vol. 99. No. 1. 1999.
[4] “Snort.” [Online]. Available: https://www.Snort.org/ (accessed Jan. 12, 2021)
[5] “Snort GitHub.” [Online]. Available: https://github.com/Snort3/Snort3 (accessed Jan. 15, 2021)
[6] “libpacp GitHub.” [Online]. Available: https://github.com/the-tcpdump-group/libpcap (accessed Feb. 22, 2021)
[7] Patel, Nainesh V., Narendra M. Patel, and Costas Kleopa. "OpenAppID-application identification framework next generation of firewalls." 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016.
[8] R Ierusalimschy, LH De Figueiredo, W Celes, “Lua 5.1 Reference Manual” 2006. [Online]. Available: https://www.lua.org/manual/5.1/manual.html (accessed Apr. 03, 2020)
[9] Ierusalimschy, Roberto, Luiz Henrique De Figueiredo, and Waldemar Celes Filho. "Lua—an extensible extension language." Software: Practice and Experience 26.6 (1996): 635-652.
[10] Reed, Andrew, and Benjamin Klimkowski. "Leaky streams: Identifying variable bitrate DASH videos streamed over encrypted 802.11 n connections." 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2016.
[11] Reed, Andrew, and Michael Kranch. "Identifying https-protected netflix videos in real-time." Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy. 2017.
[12] Gu, Jiaxi, et al. "Walls have ears: Traffic-based side-channel attack in video streaming." IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 2018.

指導教授

王尉任梁德容(Wei-Jen Wang Deron Liang)

審核日期

2021-8-20

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