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博碩士論文 etd-0729105-105839 詳細資訊
Title page for etd-0729105-105839
論文名稱
Title
以支援向量機為基礎之後門程式偵測
Backdoor Detection based on SVM
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
59
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2005-07-25
繳交日期
Date of Submission
2005-07-29
關鍵字
Keywords
支援向量機、資料探勘與分類分析、入侵偵測、後門程式
Intrusion Detection, Backdoor, Data Mining and Classification, SVM
統計
Statistics
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中文摘要
隨著電腦科技的進步,網路的使用率迅速成長,網路安全也日益被重視。根據相關資料顯示,網路惡意程式的氾濫與猖獗日益嚴重,如病毒、網蟲、後門和木馬程式…等等。而後門程式更是有明顯成長的趨勢,它可以穿越企業的安全架構,如防火牆、防毒軟體,更會竊取機密資訊、佔用網路資源、當作跳板主機、甚至進行大規模的攻擊(如分散式阻斷服務攻擊)。
在文獻中我們分析後門程式的特性與種類,探討資料挖礦和支援向量機(Support Vector Machine)於入侵偵測的應用,本研究主要會專注於後門程式連線之偵測,且提出一個偵測後門程式連線行為的架構。此架構是以支援向量機分類演算法為基礎,它是Vapnik為了解決在類神經網路上不可避免的問題所提出的方法,主要是建立在統計學習理論基礎之上的機器學習方法。
在系統模組與驗證方面,本研究選擇適合在小型區域網路的IPAudit流量監控軟體以及支援向量機的分類軟體libsvm。利用libsvm將IPAudit所擷取的封包進行交互式流量進行分類,進一步與我們所建立的合法服務列表比對,判斷後門程式連線的行為。本研究比對SVM、C4.5、Na
Abstract
With the improvement of computer technologies and the wide use of the Internet, network security becomes more and more significant. According to the relevant statistics, malicious codes such as virus, worms, backdoors, and Trojans launch a lot of attacks. Backdoors are especially critical. Not only can it cross firewalls and antivirus software but also will steal confidential information and misuse network resources and launch attacks such as DDoS(Distributed Denial of Service).
In this research, we analyze the properties and categories of backdoors and the application of data mining and support vector machines in intrusion detection. This research will focus on detecting the behavior of backdoor connection, and we propose a detecting architecture. The architecture is based on SVM, which is a machine learning method based on statistic theory and proposed by Vapnik to solve the problems in Neural Network techniques.
In system modules, this research chooses IPAudit as our network monitor and libsvm as a SVM classifier. The packets captured by IPAudit will be classified into interactive or non-interactive flow by libsvm, and the result will be compared with legal service lists to determine whether a connection is a backdoor connection. We compare the accuracy of SVM, C4.5, and Na
目次 Table of Contents
第一章、緒論 7
第一節、研究背景 7
第二節、研究動機 9
第二章、文獻探討 13
第一節、後門程式之分類 13
第二節、駭客入侵程序 15
第三節、入侵偵測 16
第四節、交互式後門程式之偵測 21
第五節、分類分析技術 22
第六節、後門實例 29
第七節、網路流量收集 31
第三章、偵測方法 36
第一節、問題定義與描述 36
第二節、LIBSVM分類分析工具 37
第三節、系統架構 40
第四節、系統設計 44
第四章、系統評估 49
第一節、SVM分類評估 49
第二節、後門程式偵測評估 52
第五章、結論與未來研究 54
參考文獻 55
英文參考文獻 55
中文參考文獻 57
參考文獻 References
英文參考文獻
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中文參考文獻
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