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研究生:蕭倫
論文名稱:多層次的P2P流量分類系統之研究
論文名稱(外文):A Study of Multi-level P2P Traffic Classification System
指導教授:劉宗杰劉宗杰引用關係
口試委員:陳青文郭育政
口試日期:2013-07-04
學位類別:碩士
校院名稱:逢甲大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:38
中文關鍵詞:流量分類多層次架構回饋機制
外文關鍵詞:P2P
相關次數:
  • 被引用被引用:0
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  • 下載下載:24
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在最近幾年的網路環境中,由於 Peer-to-peer (P2P) 應用程式的蓬勃發展,使得 P2P 應用程式的流量超過50%的整體網路總流量。使用者在使用 P2P 檔案分享軟體時往往會佔據大部分的網路頻寬,這影響了其他使用者的網路速度,降低了整體網路的效能。同時許多使用者會利用 P2P 軟體分享版權物品,違反智財權。因此對於企業或者校園管理者來說,能夠有效的偵測 P2P 應用程式流量,將 P2P 流量分類出來加以控管是有必要的。
P2P 的應用程式為了躲避流量偵測軟體的偵測,使用了許多躲避偵測的技術,例如使用動態 Port number、加密等動作。這些行為使得傳統用來分類流量的方法逐漸無法滿足現今流量分類的需求;且由於目前 P2P 應用程式的多樣性,若只用單一種流量分類方法無法有效的分類這些流量。因此本篇論文使用了多層次 (Multi-level) 的架構來設計 P2P 的流量分類系統。本論文所提的系統整合了多種流量分類的技術,系統使用階層式的判斷,擷取各層分類方法的優點來達到更準確的流量分類以及降低分類的誤判率。為了提高整體分類系統的效能,系統同時包含快速初步的分類,以及利用長時間的連線行為觀察與搭配 Machine learning 分類器來進一步的分類;本論文所提的系統加入了回饋機制,讓更多的流量能夠靠著系統回饋的結果快速從初步分類模組分類出來,以提升分類的效能。
In recent years, due to the rapid development of the P2P applications, the P2P network traffic occupies more than 50% of the overall network traffic. The users will occupy most of network bandwidth when using P2P file-sharing software which will affect other user’s network speed and reduce the overall network performance. Therefore, for network engineers, it is necessary to identify and control the P2P traffic effectively.
Many techniques used to avoid detection have been proposed such as using dynamic port number, encrypt payload, etc.; these techniques make the traditional traffic classification methods gradually unable to meet the demand of today’s traffic classification. Due to the diversity of the P2P applications, high false classification rate may occur if we only use single classification method to classify the P2P flows. Hence, this thesis proposes a multi-level P2P classification system. The system integrates multiple classification techniques to combine their advantages. It includes the fast preliminary classification and the long-term classification achieving to classify most flows fast with a low false classification rate. In addition, it also has the feedback mechanism that can let more flows be classified fast by feedback results to improve the classification speed.
誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 論文架構 2
第二章 相關文獻研究與回顧 4
2.1 Port-based 分類方法 4
2.2 Payload-based 分類方法 4
2.3 Behavioral-based 分類方法 5
2.4 Machine learning 分類方法 6
第三章 系統架構 7
3.1 系統概述 7
3.2 Trust-list module 8
3.3 Port-based module 9
3.4 Behavioral-based module 9
3.5 Machine learning module 12
第四章 利用回饋機制快速細分應用程式 15
4.1 Trust-list module 16
4.2 Port-based module 16
4.3 Behavioral-based module 16
4.4 Machine learning module 16
第五章 系統分析與實驗結果 18
5.1 實驗環境 18
5.2 Machine learning module 可信度 18
5.3 系統分類準確率與誤判率 20
5.4 Trust-list module 效能分析 22
第六章 結論 25
6.1 結論 25
6.2 未來研究目標 25
參考文獻 26
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