針對商品以及對女巫攻擊有抵抗力的信譽系統

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姓名

劉思奇(Szu-chi Liu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

針對商品以及對女巫攻擊有抵抗力的信譽系統
(A more accurate and sybil resistant reputation systemfor products)

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摘要(中)

隨著網際網路的應用不斷進步，各種藉由網際網路的電子商務行為、資訊傳播也蓬勃發展。然而，網路上的使用者在面對未知的或其不熟悉的物品時，傾向於先觀察其他人對於此物品的觀感，然後決定自己接下來採取的行動。信譽系統（Reputation system）正是一種利用推薦與評價，讓使用者藉由其他人對此物品的經驗中了解此物品之信譽評等的一種機制。
　　然而現今網路上普遍採用的信譽系統並非完美，容易受到sybil attack之攻擊，並且信譽系統所計算出之評價值未必對每個使用者都適用，因此本篇論文將提出一個更為安全以及參考價值更高的信譽系統。
　　本篇論文的信譽系統是針對物品（Product）或服務（service）來做出評價，並且與其他線上信譽系統不同之處在於，對於同一個物體，系統中每一個使用者所看到的評價都不相同，系統利用使用者給物體的評價來分辨出價值觀相近的人，再參考這些價值觀相近之使用者的評價來計算評價值。經由這種方式算出的來評價對使用者來說較有參考價值，惡意使用者想要成功發起sybil attack攻擊的難度也會大大提高。

摘要(英)

The applications of internet are growing up day by day, many e-commercial behavior and information exchange become more. However, the users on internet tend to observe other people’s experience before making decide what to do when they meet the entities they don’t familiar. The reputation system is a mechanism which using rating and recommends and let users know some entity’s reputation from other people’s experience.
　　But the reputation system used on line is not perfect, it is easy to affect by Sybil attack and the reputation value the system provide is not suitable to every user. We propose a more secure and more accurate reputation system in this paper.
　　The reputation system we propose aim at products or services. The difference between other on-line reputation systems and our reputation system is that every entity’s reputation value is difference between every user in our reputation system. The system use the votes to distinguish the users have similar values, and use the users to compute reputation value. The reputation value computing from this method is more accurate and the success attack by Sybil attack is more difficult.

關鍵字(中)

★ 女巫攻擊
★ 信譽系統

關鍵字(英)

★ sybil attack
★ reputation system

論文目次

摘要 ii
Abstract iii
誌謝 iv
目錄 v
圖目錄 vi
表目錄 vii
一、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 方法概述 2
1.4 章節架構 3
二、信譽系統應用 4
2.1 PageRank 4
2.2 電子購物網站 5
2.3 P2P網路環境 5
2.4 Anti-spam 5
2.5 商品或服務 6
2.6 討論 6
三、相關研究 7
3.1 sybil attack 7
3.1.1 DHT 7
3.1.2 無線感測網路環境 7
3.1.3 P2P網路環境 8
3.2 篩選價值觀相近使用者 9
四、架構設計 10
4.1 一般信譽系統 10
4.2 系統設計 10
4.2.1 篩選使用者 11
4.2.2 加權評價值 12
4.2.3 討論 13
五、實驗 15
5.1 Sybil attack抵抗程度 15
5.1.1 攻擊模式1 15
5.1.3 攻擊模式2 26
5.2 準確率評估 27
六、結論與未來工作 41

參考文獻

[1] P. Resnick, R. Zeckhauser, E. Friedman, K. Kuwabara. Reputation Systems. In Communications of the ACM, 2000
[2] J.R. Douceur. The Sybil Attack. In Proc. of the IPTPS02 Workshop, 2002
[3] A. Cheng, E. Friedman. Sybilproof reputation mechanisms. In SIGCOMM workshop on Economics of peer-to-peer systems, 2005
[4] Florent F. Garcin, Boi Faltings and Radu Jurca. Aggregating Reputation Feedback. In International Conference on Reputation, 2009
[5] Gautam Singaraju and Brent ByungHoon Kang. RepuScore: Collaborative Reputation Management Framework for Email Infrastructure. In USENIX,Proceedings of the 21st Large Installation System Administration Conference 2007
[6] Haifeng Yu, Michael Kaminsky, Phillip B. Gibbons, Abraham Flaxman. SybilGuard： defending against sybil attacks via social networks. In Proceeding of the ACM SIGCOMM workshop, September 2006
[7] Nan Hu, Paul A. Pavlou, Jennifer Zhang. Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication. In Proceedings of the 7th ACM conference on Electronic commerce.
[8] Wang, Y, Vassileva, J Trust and Reputation Model in Peer-to-Peer Networks In Proc Peer-to-Peer Computing, 2003.
[9] Ernesto Damiani, De Capitani di Vimercati, Stefano Paraboschi, Pierangela Samarati, Fabio Violante. A Reputation-Based Approach for Choosing Reliable Resources in Peer-to-Peer Networks. In Proceedings of the 9th ACM conference on Computer and communications security 2002.
[10] Bradley Taylor. Sender Reputation in a Large Webmail Service. Third Conference On Email and Anti-Spam 2006.
[11] Guido Urdaneta, Guillaume Pierre, Maarten van Steen. A Survey of DHT Security Techniques. Accepted for publication in ACM Computing Surveys, 2009
[12] George Danezis, Chris Lesniewski-Laas, M. Frans Kaashoek, Ross Anderson. Sybil-resistant DHT routing. In Proc 10th European Symposium on Research in Computer Security, Milan, Italy, September 12-14, 2005.
[13] J. Newsome, E. Shi, D. Song, andA. Perrig. The Sybil Attack in Sensor Networks Analysis & Defenses. In Proc. Intl Sympon Information Processing in SensorNetworks, 2004.
[14] Piro Chris, Shields Clay, Levine Brian Neil. Detecting the Sybil Attack in Mobile Ad hoc Networks. In Proc. IEEE/ACM Intl Conf on Security and Privacy in Communication Networks(SecureComm), August 2006
[15] Brian Neil Levine, Clay Shields, N. Boris Margolin. A Survey of Solutions to the Sybil Attack. Tech report 2006-052, University of Massachusetts Amherst, Amherst, MA, October 2006
[16] Greg Linden, Brent Smith, and Jeremy York. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, v.7 n.1, p. 76-80, January 2003
[17] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based Collaborative Filtering Recommendation Algorithms. In proc of the 10th international conference on World Wide Web, 2001.
[18] Kevin Walsh, Kevin Walsh. Fighting peer-to-peer SPAM and decoys with object reputation. In Proceeding of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems.
[19] SpamCop. What is the SpamCop Blocking List(SCBL)? www.spamcop.net/bl.html
[20] Wong, M. W. Sender Authentication: What To Do, Technical Document, 2004, http://www.openspf.org/whitepaper.pdf
[21] DomainKeys Identified Mail (DKIM) Service Overview http://www.dkim.org/specs/draft-ietf-dkim-overview-10.html

指導教授

許富皓(Fu-Hau Hsu)

審核日期

2009-8-22

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