Defending multiple-user-multiple-target attacks in online reputation systems

Yuhong Liu, Yan Sun, Ting Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

As online reputation systems are playing increasingly important roles in reducing risks of online interactions, attacks against such systems have evolved rapidly. Nowadays, some powerful attacks are conducted by companies that make profit through manipulating reputation of online items for their customers. These items can be products (e.g. in Amazon), businesses (e.g. hotels in travel sites), and digital content (e.g. videos in Youtube). In such attacks, colluded malicious users play wellplanned strategies to manipulate reputation of multiple target items. To address these attacks, we propose a defense scheme that (1) sets up heterogeneous thresholds for detecting suspicious items and (2) identifies target items based on correlation analysis among suspicious items. The proposed scheme and two other comparison schemes are evaluated by a combination of real user data and simulation data. The proposed scheme demonstrates significant advantages in detecting malicious users, recovering reputation scores of target items, and reducing interference to normal items.

Original languageEnglish
Title of host publicationProceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
Pages425-434
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011 - Boston, MA, United States
Duration: 9 Oct 201111 Oct 2011

Other

Other2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011
CountryUnited States
CityBoston, MA
Period9/10/1111/10/11

Fingerprint

Online systems
Hotels
Industry
Profitability

ASJC Scopus subject areas

  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

Cite this

Liu, Y., Sun, Y., & Yu, T. (2011). Defending multiple-user-multiple-target attacks in online reputation systems. In Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011 (pp. 425-434). [6113144] https://doi.org/10.1109/PASSAT/SocialCom.2011.227

Defending multiple-user-multiple-target attacks in online reputation systems. / Liu, Yuhong; Sun, Yan; Yu, Ting.

Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011. 2011. p. 425-434 6113144.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, Y, Sun, Y & Yu, T 2011, Defending multiple-user-multiple-target attacks in online reputation systems. in Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011., 6113144, pp. 425-434, 2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011, Boston, MA, United States, 9/10/11. https://doi.org/10.1109/PASSAT/SocialCom.2011.227
Liu Y, Sun Y, Yu T. Defending multiple-user-multiple-target attacks in online reputation systems. In Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011. 2011. p. 425-434. 6113144 https://doi.org/10.1109/PASSAT/SocialCom.2011.227
Liu, Yuhong ; Sun, Yan ; Yu, Ting. / Defending multiple-user-multiple-target attacks in online reputation systems. Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011. 2011. pp. 425-434
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