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An unsupervised strategy for defending against multifarious reputation attacks

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Abstract

In electronic markets, malicious sellers often employ reviewers to carry out different types of attacks to improve their own reputations or destroy their opponents’ reputations. As such attacks may involve deception, collusion, and complex strategies, maintaining the robustness of reputation evaluation systems remains a challenging problem. From a platform manager’s view, no trader can be taken as a trustable benchmark for reference, therefore, accurate filtration of dishonest sellers and fraud reviewers and precise presentation of users’ reputations remains a challenging problem. Based on impression theory, this paper presents an unsupervised strategy, which first design a nearest neighbor search algorithm to select some typical lenient reviewers and strict reviewers. Then, based on these selected reviewers and the behavior expectation theory in impression theory, this paper adopts a classification algorithm that pre-classify sellers into honest and dishonest ones. Thirdly, another classification algorithm is designed to classify reviewers (i.e., buyers) into honest, dishonest, and uncertain ones according to their trading experiences with the pre-classified sellers. Finally, based on the ratings of various reviewers, this paper proposes a formula to estimate seller reputations. We further designed two general sets of experiments over simulated data and real data to evaluate our scheme, which demonstrate that our unsupervised scheme outperforms benchmark strategies in accurately estimating seller reputations. In particular, this strategy can robustly defend against various common attacks and unknown attacks.

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Acknowledgements

This paper is supported in part by the Natural Science Foundation of China (No. 71772107, 71403151, 61502281, 61433012, U1435215), Qingdao social science planning project (No. QDSKL1801138), the training program of the major research plan of the National natural science foundation of China (No. 91746104), the National Key R&D Plan of China (Nos. 2017YFC0804406, 2018YFC0831002), Humanity and Social Science Fund of the Ministry of Education (No. 18YJAZH136), the Key R&D Plan of Shandong Province (No.2018GGX101045), the Natural Science Foundation of Shandong Province (Nos. ZR2018BF013, ZR2013FM023, ZR2014FP011, ZR2019MF003), Shandong Education Quality Improvement Plan for Postgraduate, the Leading talent development program of Shandong University of Science and Technology and Special funding for Taishan scholar construction project and SDUST Research Fund.

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Authors and Affiliations

  1. Network and Information Center, Shandong University of Science and Technology, Qingdao, 266590, China

    Xin Wang

  2. Key Laboratory for wisdom mine information technology of Shandong Province, Shandong University of Science and Technology, Qingdao, 266590, China

    Shu-juan Ji & Yong-quan Liang

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Ho-fung Leung

  4. Faculty of Education, The University of Hong Kong, Hong Kong, China

    Dickson K.W. Chiu

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  1. Xin Wang
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  2. Shu-juan Ji
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  4. Ho-fung Leung
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  5. Dickson K.W. Chiu
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Correspondence to Shu-juan Ji.

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Wang, X., Ji, Sj., Liang, Yq. et al. An unsupervised strategy for defending against multifarious reputation attacks. Appl Intell 49, 4189–4210 (2019). https://doi.org/10.1007/s10489-019-01490-9

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