博碩士論文 985202065 詳細資訊




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姓名 林松輝(Song-Hui Lin)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱
(FFRTD: Beat Fast-Flux by Response Time Differences)
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摘要(中) 近年來,Fast-Flux Service Network (FFSN) 在網際網路上已經造成重大的威脅,它的成員主要來自大量遭惡意程式感染的電腦。攻擊者利用這類攻擊手法發起一系列的違法行為,舉凡分散式阻斷服務攻擊、發送垃圾郵件、架釣魚網站和散佈惡意程式等。由於 FFSN 本身具有高度的隱蔽性,我們難以將攻擊者繩之以法,也無法輕易摘除整個有害的網路服務。
在本篇論文中,我們發現一種簡單且新穎的特徵─difference,它是用來衡量一個 fast-flux 網域名稱其對應所有主機之負載平衡的變化程度。我們也提出了一套偵測系統 FFRTD,它使用 difference 特徵搭配 DNS 的查詢結果,讓我們可以在兩小時以內將一個全新的網域名稱分類成「正常 (benign)」或「fast-flux」。而由我們的方法中,在做分類的同時,並不需要存取資料庫,只需要利用培訓資料 (training data) 過程中所產生的門檻值 (ff-score threshold)。本研究的實驗結果證明,我們所提出的偵測系統能夠準確地判斷出身陷 FFSNs 的網域名稱,並且我們也開拓一個新的觀察視野,對於了解一個 fast-flux 網域名稱將會很有幫助。
摘要(英) FFSNs have become severe threats on the Internet in recent years. They consist of a large amount of compromised hosts for malicious activities such as launching DDoS, delivering spam mails, hosting phishing sites and distributing malicious programs. As a result of the highest concealment of FFSNs, it is really difficult to find out attackers and foil down the entire illegal networks. In this paper, we discovered a novel and simple feature, difference, which measures the degree of the load balance of all IP addresses in a domain name. And we also present FFRTD that can make a brand-new domain name be classified into benign and fast-flux ones by the difference with DNS lookup results within two hours. With our method, there is no need to access database but use the ff-score threshold we generated in the training phase while classifying domain names. According to experimental results, our proposed detection system, FFRTD, is able to accurately detect FFSNs. Furthermore, we contribute a new vision to observe the behavior of a fast-flux domain name.
關鍵字(中) 關鍵字(英) ★ FFSN
★ RRDNS
★ CDN
論文目次 摘要................................................i
Abstract...........................................ii
Table of Contents.................................iii
List of Figures.....................................v
List of Tables....................................vii
1. Introduction..................................1
2. Background....................................4
2.1 Related DNS Techniques.......................4
2.1.1 DNS........................................4
2.1.2 FQDN.......................................4
2.1.3 DNS Responses..............................5
2.1.4 RRDNS......................................6
2.1.5 CDNs.......................................7
2.2 Fast Flux....................................9
2.2.1 FFSNs......................................9
2.2.2 Two Types of FFSNs........................11
2.2.3 The Threat on FFSNs.......................13
3. Related Work.................................15
4. Design of FFRTD..............................18
4.1 Feature Extraction..........................18
4.1.1 Related to the Feature of Real Time.......18
4.1.2 Discover a Feature of Short Time..........21
4.2 Measure the Difference for Analysis.........23
4.3 Architecture of FFRTD.......................26
4.3.1 Three Major Components....................26
4.3.2 Two Process Phases........................31
5. Experimental Results.........................33
5.1 Training Data Sets..........................33
5.2 Testing Data Sets...........................37
5.3 Evaluation..................................41
5.3.1 Detection Accuracy........................41
5.3.2 Detection Performance.....................42
6. Discussion...................................43
6.1 Limitations.................................43
6.2 Deployment..................................43
6.3 Future work.................................44
6.4 Conclusion..................................45
Reference..........................................46
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[4] ICANN, GNSO Issues Report on Fast Flux Hosting. March 2008.
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[9] Gartner, Inc., Gartner Says Number of Phishing E-Mails Sent to U.S. Adults Nearly Doubles in Just Two Years, November 9, 2006. Available from: http://www.gartner.com/it/page.jsp?id=498245.
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[15] Holz, T., Gorecki, C., Rieck, K., and Freiling, F.C., "Measuring and Detecting Fast-Flux Service Networks", in Network and Distributed System Security Symposium, San Diego, CA, February 2008.
[16] RRDNS, Round Robin DNS Information and Services. Available from: http://www.rrdns.com/.
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[20] Perdisci, R., Corona, I., Dagon, D., and Lee, W., "Detecting Malicious Flux Service Networks through Passive Analysis of Recursive DNS Traces", in Proceedings of the 2009 Annual Computer Security Applications Conference, pp. 311-320, IEEE Computer Society, 2009.
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[23] Hsu, C.-H., Huang, C.-Y., and Chen, K.-T., "Fast-flux bot detection in real time", in Proceedings of the 13th international conference on Recent advances in intrusion detection, pp. 464-483, Springer-Verlag, Ottawa, Ontario, Canada, September 2010.
[24] Alexa the Web Information Company. Available from: http://www.alexa.com/.
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[26] dnsbl.abuse.ch. Available from: http://dnsbl.abuse.ch/fastfluxtracker.php.
指導教授 許富皓(Fu-Hau Hsu) 審核日期 2011-7-28
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