博碩士論文 105522082 詳細資訊




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姓名 黃啟澤(Qi-Ze Huang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 基於完全合格域名之邊緣網路封包分類器
(FQDN-based Packet Classifier on Edge Networks)
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摘要(中) 近年來,邊緣運算的概念逐漸萌芽,由於眾多的物聯網裝置接入廣域網路中,雖然單點裝置運算能力薄弱,但由於數量龐大,綜觀而言是一群不容小覷的邊緣網路運算節點。然而,由於愈來愈多種類的裝置和新的網路協定出現於廣域網路中,訊務分類的難度也愈來愈高,許多研究嘗試使用深度封包檢測、機器學習或網域名稱(DNS)萃取的方式,多數對於內容加密封包之分類效果有限,且無法直接實際部署於真實網路環境中。
  本論文提出之FIPAC封包分類機制,從邊緣網路著手,相較於核心網路,能獲取更多完全合格域名(FQDN)之資訊,配合維基百科和自治系統號碼來分類域名,並透過封包各層資訊追蹤連線,藉此達到自動分類和快速推斷的效果。和其他種分類器相比而言,FIPAC機制更人性化、運算資源需求更少、分類效率更高,也更能維護使用者隱私。有了FIPAC機制,網路維運人員不需要擔心分類機制占用過多運算資源,能夠直接專注在分類結果的運用上,如不同應用程式QoS的控管。
  本論文也使用了軟體路由器,將FIPAC機制部署在邊緣網路中,和市售邊緣網路路由器以及其他種分類器作比較,以實際驗證FIPAC機制的效能和部署的靈活性。
摘要(英) In recent years, the concept of edge computing comes up. Due to the access of many IoT devices to wide-area networks, although the computing power of single node is weak, it offers great possibility when they are grouped. However, as more and more devices and new network protocols appear in the WAN, traffic classification becomes more and more difficult. Many researches attempt to use deep packet inspection, machine learning or domain name system (DNS), which are insufficient for encrypted packages and cannot be directly deployed in real network environments.
The FIPAC packet classification mechanism proposed in this paper are intentionally deployed on edge networks for the reason that more fully qualified domain names (FQDNs) are obtained more easily in comparison to core networks. Then, the FQDN can be classified with effective labels fetched from Wikipedia entries and autonomous system number. This is how FIPAC achieves automatic classification and fast inference. Compared with other classifiers, the FIPAC mechanism is more user-friendly and efficient, also, requires less computing resources and respects users’ privacy. With FIPAC, network operators do not need to worry about exhausted computing resources taken by packet classifiers. They can focus on the utilization of classification results, such as QoS control on distinct applications.
In this paper, we deploy FIPAC on software router on real edge networks. Compared with commercially available embedded edge routers and other types of classifiers, we verify that FIPAC takes advantages in performance and flexibility.
關鍵字(中) ★ 即時封包分類器
★ 完全合格域名
★ 網域名稱系統
★ 軟體路由器
★ 機器學習
★ 深度封包檢測
關鍵字(英) ★ Real-time Packet Classifier
★ FQDN
★ DNS
★ Software Router
★ Machine Learning
★ Deep Packet Inspection
論文目次 第一章 緒論 1
1.1 概要 1
1.2 研究動機 2
1.3 研究目的 2
1.4 論文架構 3
第二章 背景知識與相關研究 4
2.1 封包分類器相關技術 4
2.1.1 基於網路通訊埠之分類器(Port-based Classifier) 4
2.1.2 基於深度封包檢測之分類器(DPI-based Classifier) 5
2.1.3 基於機器學習之分類器(ML-based Classifier) 10
2.1.4 基於網域名稱系統之分類器(DNS-based Classifier) 14
2.2 邊緣運算 16
2.3 相關文獻比較 18
2.3.1 是否能自動產生測試流量 18
2.3.2 需要安裝代理軟體與否 18
2.3.3 服務質量(QoS)之控制 19
2.3.4 封包鏡像 19
2.3.5 分類模型預先訓練 19
第三章 研究方法 21
3.1 系統架構與設計 21
3.1.1 Router Function各模組 22
3.1.2 Databases 24
3.1.3 SQLite3 API模組 25
3.1.4 FIPAC Parsing Stage各模組 25
3.1.5 FIPAC Classification Stage各模組 28
3.1.6 FIPAC Redirection Stage各模組 30
3.2 系統運作流程與機制 30
3.2.1 系統假設與定義 30
3.2.2 資料符號表 32
3.2.3 FIPAC運作流程 34
3.3 系統實作 47
第四章 實驗與討論 50
4.1 情境一:FIPAC機制之效能量測 50
4.1.1 實驗一:邊緣路由器效能量測(單個客戶端裝置) 50
4.1.2 實驗二:邊緣路由器效能量測(五十個客戶端) 53
4.2 情境二:FIPAC機制之功能驗證 54
4.2.1 實驗三:邊緣路由器線上部署FIPAC機制 54
4.2.2 實驗四:網頁服務辨識 55
4.2.3 實驗五:熱門應用程式辨識 58
4.2.4 實驗六:作業系統相關服務辨識 60
4.2.5 實驗七:非熱門服務辨識 61
4.3 情境三:服務質量控制(QoS) 63
4.3.1 實驗八:Youtube服務質量控制 63
4.3.2 實驗九:惡意廣告網域封鎖 64
第五章 結論與未來研究方向 67
5.1 結論 67
5.2 研究限制 67
5.3 未來方向 68
5.3.1 P4交換器 68
5.3.2 IPv6 68
5.3.3 使用者隱私 69
參考文獻 70
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指導教授 周立德(Li-Der Chou) 審核日期 2018-8-23
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