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博碩士論文 etd-0727113-234333 詳細資訊
Title page for etd-0727113-234333
論文名稱
Title
植基於異常網路行為之集中式殭屍網路偵測機制
Detecting Centralized Botnets based on Anomaly Traffic Behaviors
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
68
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
2013-07-29
繳交日期
Date of Submission
2013-08-29
關鍵字
Keywords
殭屍網路、集中式殭屍網路、偵測、相似度分析、相關分析
Centralized Botnet, Botnet, Detection, Correlation Analysis, Similarity Analysis
統計
Statistics
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中文摘要
殭屍網路(Botnet)已經對於Internet的穩定性造成重大威脅。目前許多攻擊手法,諸如垃圾郵件、釣魚網站、身份竊取、分散式阻斷服務攻擊(Distributed Denial of Service, DDoS)等,皆透過殭屍網路實現,對許多企業與組織造成重大傷害。
目前殭屍網路控制方式以集中式殭屍網路(Centralized Botnet)為主,究其原因為該類型殭屍網路易於操控之特性。集中式殭屍網路包含IRC-based與HTTP-based殭屍網路。本研究發現集中式殭屍網路的Homogeneous Response,Group Activity與 Periodic Connection可用來偵測集中式殭屍網路。本研究所提出之集中式殭屍網路偵測機制透過Payload相似度分析(Payload Similarity Analysis)與行為相關分析(Behavior Correlation Analysis)偵測集中式殭屍網路。
本研究所提出之偵測方法可以在攻擊者命令殭屍網路發動攻擊前有效區別受感染主機與未受感染主機產生之可疑訊息,發現受感染主機之惡意行為以及識別殭屍網路之存在。研究結果顯示本研究所提出之偵測方法可有效偵測集中式殭屍網路。
Abstract
Cybercrime presents great security challenges for Internet and makes Internet security gain a lot of attention by academic domain. Cybercrime takes advantage of Internet for gaining revenue and profit, sending malicious mail, compromising computer hosts, stealing confidential information and launching distributed denial of service (DDoS) attacks. Cyber-attacks which compromise the security such as confidentiality, integrity, availability of a computer and network system are often carried out by botnets. Botnets have become a serious threat to the stability of Internet, because they can cause huge disasters to organizations and are difficult to detect their existence.
Most of existing botnets are centralized botnets because of ease of use and control. Centralized botnets often use IRC (Internet Relay Chat) or HTTP as a communication channel through which the botmaster can control the IRC-based or HTTP-based botnets to propagate more infections or launch attacks. There are three distinct message patterns of centralized botnets discovered from our observation: the homogeneous response, the group activity, and the periodic connection. The set of infected hosts respond similar message to the botmaster, exhibiting homogenous response. A group of hosts with long time span act together shows the characteristic of the group activity. The HTTP bots need to periodically perform a set of inquiries to retrieve the commands and messages which are prepared and maintained by the botmaster. In this research, a centralized botnet detection approach is proposed to identify the bots and botnet by the payload similarity analysis, autocorrelation analysis, and cross correlation analysis. The payload similarity analysis is employed to measure the similarity between flows. The autocorrelation and cross correlation analysis are employed to discover the periodic behaviors of centralized bots.
The proposed method can differentiate the suspicious messages generated by infected hosts (bots) from normal clients, discover the malicious behaviors of bots and identify the existence of bonnet before the botmaster launches attacks. The experimental results show that the proposed approach effectively detects abnormal behaviors of centralized botnets and the existence of centralized botnets.
目次 Table of Contents
中文摘要 i
Abstract ii
Table of Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
1.1 Research Background 2
1.2 Research Motivation 4
1.3 Research Contribution 6
Chapter 2 Related Work 8
2.1 Botnet C&C Models 8
2.2 The Evolution of Botnet 11
2.3 Botnet Life Cycle 13
2.4 Botnet Detection Approaches 16
Chapter 3 Proposed Approach 21
3.1 Feature Extraction 22
3.2 Flow Aggregation 24
3.3 Payload Similarity Analysis 26
3.3.1 Longest Common Subsequence 26
3.3.2 Normalized Compression Distance 29
3.4 Behavior Correlation Analysis 31
3.4.1 Cross-Correlation Coefficient 31
3.4.2 Autocorrelation Coefficient 32
Chapter 4 System Evaluation 34
4.1. Data Collection 34
4.2. Evaluation Metric and Experimental Design 38
4.3 Experimental Results 40
4.3.1 IRC-based Botnet Detection 40
4.3.2 HTTP-based Botnet Detection 44
4.3.3 Performance Comparison - BotHunter 45
4.3.4 Parameter Analysis 47
Chapter 5 Conclusion 50
Chapter 6 Future Work 52
Bibliography 53
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