基於數位分身實現網路拓樸異常流量監測之實作

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姓名

陳羿茜(Yi-Cian Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於數位分身實現網路拓樸異常流量監測之實作
(Implementation of Network Topology Anomaly Traffic Monitoring Based on Digital Twin)

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至系統瀏覽論文 (2026-9-1以後開放)

摘要(中)

數位分身（DT）是一種先進的模擬技術，通過創建物理實體的虛擬分身，使我們能夠在虛擬環境中實時模擬和分析實體的運行狀態。這一技術不僅能夠提高系統的可視化和監控能力，而且也被廣泛應用於各種行業中，包括製造業、醫療、城市規劃以及網路技術，並在這些領域中帶來許多顯著的改變和創新。
在本論文中，我們探索了數位分身技術在網路拓樸異常流量監測中的應用，通過兩台虛擬機（VM）來模擬和分析網路異常狀態對於網路拓樸的影響。第一台虛擬機（VM1）代表實體網路，而第二台虛擬機（VM2）則作為虛擬環境，兩者均安裝了Mininet並運行相同的拓樸腳本以保持網路拓樸的一致性。
在VM1上，我們利用Scapy工具捕捉並過濾出ICMP流量的來源和目的地地址，將這些數據轉換成JSON格式後，通過Socket技術傳送到VM2。接收到數據後，VM2在其虛擬拓樸上重播這些流量，以此來模擬實體網路的行為。此外，VM1會持續運行正常的網路使用流量，而VM2除了同步正常流量數據外，還額外執行異常流量模擬。
透過這一設定，我們能夠在虛擬環境中重現實體網路的運行狀態，並對比正常與異常流量下的網路行為。這不僅增進了我們對網路異常影響的理解，也驗證了數位分身技術在網路安全監測和管理中的實用性。本論文的成果顯示，利用數位分身技術可以有效地在沒有干擾實體網路運作的情況下，充分利用實時數據對網路異常進行模擬與分析，這對於設計更為安全與穩定的網路系統具有重要意義，包括在虛擬環境中重現各種異常情境，使網路管理者能夠提前制定應對策略，提高實際運行中的應變能力；通過虛擬環境中的模擬與測試，避免在實體網路上進行實驗，從而減少因測試引起的停機時間；根據模擬結果對網路資源進行更為合理的配置，優化網路性能，提升整體運營效率。

摘要(英)

Digital Twin (DT) technology is an advanced simulation technique that creates virtual replicas of physical entities, enabling real-time simulation and analysis of their operational states within a virtual environment. This technology enhances system visualization and monitoring capabilities and is applied across diverse industries including manufacturing, healthcare, urban planning, and network technology, where it drives significant innovations and changes.
In this thesis, we explore the application of DT technology in monitoring anomaly traffic within network topologies. We employ two virtual machines (VMs): VM1, which represents the physical network, and VM2, which acts as the virtual environment. Both VMs are equipped with Mininet and execute identical topology scripts to ensure consistent network topology across both environments. On VM1, the Scapy tool is used to capture and filter the source and destination addresses of Internet Control Message Protocol (ICMP) traffic. This data is converted into JavaScript Object Notation (JSON) format and transmitted to VM2 using Socket technology. VM2 then replays this traffic on its virtual topology to simulate the behavior of the physical network.
Through this setup, we replicate physical network operations in a virtual environment to compare normal and abnormal traffic behaviors. This enhances our understanding of network anomalies and validates digital twin technology for network security monitoring. Our findings show that digital twins can simulate and analyze network anomalies in real-time without disrupting physical operations. This is crucial for designing secure and stable networks, allowing administrators to anticipate and respond to anomalies, improve resilience, reduce downtime, and optimize performance.

關鍵字(中)

★ 數位分身 Digital Twin
★ 異常流量監測
★ Mininet

關鍵字(英)

★ Digital Twin
★ Anomaly Traffic Monitoring
★ Mininet

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 章節概要 3
第二章相關研究 4
2.1 數位分身的理論基礎 4
2.1.1 數位分身的建模與模擬 4
2.1.2 數據融合 4
2.1.3 交互作用與協同合作 5
2.1.4 服務 5
2.2 人工智能、機器學習與大數據在數位分身中的角色 5
2.3 網路數位分身 7
2.4 相關文獻 11
2.5 實作相關技術 18
2.5.1 Mininet 18
2.5.2 Scapy 19
2.6 即時監控系統 19
2.6.1 Grafana 20
2.6.2 Prometheus 20
2.6.3 Telegraf 22
第三章研究方法 24
3.1 拓樸呈現 25
3.2 流量同步 26
3.3 流量生成 28
3.4 異常流量生成 30
3.5 觀察與呈現 31
第四章模擬結果與分析 32
4.1 實作結果 32
4.1.1 Case 1：調整頻寬觀察異常流量下的RTT 34
4.1.2 Case 2：比較頻寬與成本之間的效益 36
4.1.3 Case 3：新增「set_bw」指令自定義修改的頻寬值 37
4.2 即時監控系統 39
4.2.1 系統架構 39
4.2.2 可視化界面 42
第五章結論 44
參考文獻 45

參考文獻

[1] F. Tao, H. Zhang, A. Liu and A. Y. C. Nee, "Digital Twin in Industry: State-of-the-Art," in IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405-2415, 2019.
[2] M. M. Rathore, S. A. Shah, D. Shukla, E. Bentafat and S. Bakiras, "The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities," in IEEE Access, vol. 9, pp. 32030-32052, 2021.
[3] P. Almasan, M. Ferriol-Galmés, J. Paillisse, J. Suárez-Varela, D. Perino, D. López, et al., "Digital twin network: Opportunities and challenges", arXiv:2201.01144, 2022.
[4] D. Chen, C. Zhou, H. Yang, M. Li and L. Lu, "The Data Domain Construction of Digital Twin Network," 2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI), Orlando, FL, USA, pp. 1-5, 2023.
[5] M. Sanz Rodrigo, D. Rivera, J. I. Moreno, M. Àlvarez-Campana and D. R. López, "Digital Twins for 5G Networks: A Modeling and Deployment Methodology," in IEEE Access, vol. 11, pp. 38112-38126, 2023.
[6] S. Zhang, C. Kang, Z. Liu, J. Wu and C. Ma, "A Product Quality Monitor Model With the Digital Twin Model and the Stacked Auto Encoder," in IEEE Access, vol. 8, pp. 113826-113836, 2020.

[7] S. Venkatesan, K. Manickavasagam, N. Tengenkai and N. Vijayalakshmi, "Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin," in IET Electr. Power Appl., vol. 13, no. 9, pp. 1328-1335, 2019.
[8] L. Zhao, G. Han, Z. Li and L. Shu, "Intelligent Digital Twin-Based Software-Defined Vehicular Networks," in IEEE Network, vol. 34, no. 5, pp. 178-184, 2020.
[9] I. Turcanu, G. Castignani and S. Faye, "On the Integration of Digital Twin Networks into City Digital Twins: Benefits and Challenges," in IEEE 21st Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, pp. 752-758, 2024.
[10] S. Vakaruk, A. Mozo, A. Pastor and D. R. López, "A Digital Twin Network for Security Training in 5G Industrial Environments," in 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), Beijing, China, pp. 395-398, 2021.
[11] [Online]https://github.com/mininet/mininet [Accessed May. 21, 2024]
[12] [Online]https://grafana.com/ [Accessed May. 21, 2024]
[13] [Online]https://prometheus.io/ [Accessed May. 21, 2024]
[14] [Online]https://www.influxdata.com/time-series-platform/telegraf/ [Accessed May. 21, 2024]
[15] [Online]https://www.youtube.com/playlist?list=PLmOn9nNkQxJHKZd1G73C3Ip86ESuSKE_u [Accessed May. 21, 2024]
[16] [Online]https://blog.techbridge.cc/2019/08/26/how-to-use-prometheus-grafana-in-flask-app/ [Accessed May. 21, 2024]

指導教授

陳彥文(Yen-Wen Chen)

審核日期

2024-7-25

推文