File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A pre-evolutionary advisor list generation strategy for robust defensing reputation attacks

TitleA pre-evolutionary advisor list generation strategy for robust defensing reputation attacks
Authors
Issue Date2016
PublisherElservier. The Journal's web site is located at http://www.elsevier.com/locate/knosys
Citation
Knowledge-Based Systems, 2016, v. 103, p. 1-18 How to Cite?
AbstractTrust and reputation systems are vital in large open distributed electronic commerce environments. Although existing various mechanisms have been adopted to guarantee trust between customers and sellers (or platforms), self-interested agents often impose various attacks to trust and reputation systems. As these attacks are usually deceptive, collusive, or strategic, it is difficult to keep trust and reputation systems robust to multifarious attacks. Many defense strategies employ a robust trust network (such as a trustable advisor list) for protecting buyers. However, in the evolution of a trust network, existing strategies consider only historical ratings of given buyers and advisors, while neglecting the timeliness of these ratings. Besides, only a single trust network is utilized to evaluate all sellers, leading to problems such as lack of pertinence and quite large deviation of evaluation. This paper proposes a novel pre-evolutionary advisor generation strategy, which first pre-evolves an optimal advisor list for each candidate seller before each trade and then evaluate each seller according to its corresponding list. After evaluating and selecting the seller, the buyer's own advisor list is evolved based on the pre-evolved optimal advisor list of chosen seller. Two sets of experiments have been designed to verify the general performance of this strategy, including accuracy, robustness, and stability. Results show that our strategy outperforms existing ones, especially when attackers use popular attack strategies such as Sybil, Sybil and Camouflage, and Sybil and Whitewashing. Besides, our strategy is more stable than compared ones, and its robustness will not change with the ratio of dishonest buyers.
Persistent Identifierhttp://hdl.handle.net/10722/231400

 

DC FieldValueLanguage
dc.contributor.authorJi, S-
dc.contributor.authorMa, H-
dc.contributor.authorZhang, S-
dc.contributor.authorLeung, HF-
dc.contributor.authorChiu, KWD-
dc.contributor.authorZhang, C-
dc.contributor.authorFang, X-
dc.date.accessioned2016-09-20T05:22:51Z-
dc.date.available2016-09-20T05:22:51Z-
dc.date.issued2016-
dc.identifier.citationKnowledge-Based Systems, 2016, v. 103, p. 1-18-
dc.identifier.urihttp://hdl.handle.net/10722/231400-
dc.description.abstractTrust and reputation systems are vital in large open distributed electronic commerce environments. Although existing various mechanisms have been adopted to guarantee trust between customers and sellers (or platforms), self-interested agents often impose various attacks to trust and reputation systems. As these attacks are usually deceptive, collusive, or strategic, it is difficult to keep trust and reputation systems robust to multifarious attacks. Many defense strategies employ a robust trust network (such as a trustable advisor list) for protecting buyers. However, in the evolution of a trust network, existing strategies consider only historical ratings of given buyers and advisors, while neglecting the timeliness of these ratings. Besides, only a single trust network is utilized to evaluate all sellers, leading to problems such as lack of pertinence and quite large deviation of evaluation. This paper proposes a novel pre-evolutionary advisor generation strategy, which first pre-evolves an optimal advisor list for each candidate seller before each trade and then evaluate each seller according to its corresponding list. After evaluating and selecting the seller, the buyer's own advisor list is evolved based on the pre-evolved optimal advisor list of chosen seller. Two sets of experiments have been designed to verify the general performance of this strategy, including accuracy, robustness, and stability. Results show that our strategy outperforms existing ones, especially when attackers use popular attack strategies such as Sybil, Sybil and Camouflage, and Sybil and Whitewashing. Besides, our strategy is more stable than compared ones, and its robustness will not change with the ratio of dishonest buyers.-
dc.languageeng-
dc.publisherElservier. The Journal's web site is located at http://www.elsevier.com/locate/knosys-
dc.relation.ispartofKnowledge-Based Systems-
dc.titleA pre-evolutionary advisor list generation strategy for robust defensing reputation attacks-
dc.typeArticle-
dc.identifier.emailChiu, KWD: dchiu88@hku.hk-
dc.identifier.doi10.1016/j.knosys.2016.03.015-
dc.identifier.hkuros266918-
dc.identifier.volume103-
dc.identifier.spage1-
dc.identifier.epage18-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats