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Article: An unsupervised strategy for defending against multifarious reputation attacks

TitleAn unsupervised strategy for defending against multifarious reputation attacks
Authors
KeywordsReputation attack
Nearest neighbor search
Lenient reviewer
Strict reviewer
Behavior expectation theory
Issue Date2019
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-669X
Citation
Applied Intelligence, 2019, v. 49 n. 12, p. 4189-4210 How to Cite?
AbstractIn 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.
Persistent Identifierhttp://hdl.handle.net/10722/285326
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 1.193
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, X-
dc.contributor.authorJi, SJ-
dc.contributor.authorLiang, YQ-
dc.contributor.authorLeung, HF-
dc.contributor.authorChiu, DKW-
dc.date.accessioned2020-08-18T03:52:26Z-
dc.date.available2020-08-18T03:52:26Z-
dc.date.issued2019-
dc.identifier.citationApplied Intelligence, 2019, v. 49 n. 12, p. 4189-4210-
dc.identifier.issn0924-669X-
dc.identifier.urihttp://hdl.handle.net/10722/285326-
dc.description.abstractIn 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.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-669X-
dc.relation.ispartofApplied Intelligence-
dc.rightsThis is a post-peer-review, pre-copyedit version of an article published in Applied Intelligence. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10489-019-01490-9-
dc.subjectReputation attack-
dc.subjectNearest neighbor search-
dc.subjectLenient reviewer-
dc.subjectStrict reviewer-
dc.subjectBehavior expectation theory-
dc.titleAn unsupervised strategy for defending against multifarious reputation attacks-
dc.typeArticle-
dc.identifier.emailChiu, DKW: dchiu88@hku.hk-
dc.description.naturepostprint-
dc.identifier.doi10.1007/s10489-019-01490-9-
dc.identifier.scopuseid_2-s2.0-85067660132-
dc.identifier.hkuros312764-
dc.identifier.volume49-
dc.identifier.spage4189-
dc.identifier.epage4210-
dc.identifier.isiWOS:000496315100007-
dc.publisher.placeUnited States-
dc.identifier.issnl0924-669X-

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