Journal
KNOWLEDGE-BASED SYSTEMS
Volume 103, Issue -, Pages 1-18Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2016.03.015
Keywords
Trust and reputation systems; Unfair rating attacks; Robust trust network; Evolutionary algorithm
Categories
Funding
- Natural Science Foundation of China [71403151, 71303140, 61572035, 61402011, 61433012, 61272153, 61502281]
- Natural Science Foundation of Shandong Province [ZR2013FM023, ZR2013FQ030, ZR2014FP011, ZR2011FL002]
- China's Post-doctoral Science Fund [2014M561948]
- Postdoctoral innovation project special funds of Shandong Province [201403007]
- Applied research project for Qingdao postdoctoral researcher
- Qingdao Science and Technology Development Project [KJZD-13-29-JCH]
- Project of Shandong Province Higher Educational Science and Technology Program [J14LN33]
- Leading talent development program of Shandong University of Science and Technology
- Special Project fund of Taishan scholar of Shandong Province
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Trust 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. (C) 2016 Elsevier B.V. All rights reserved.
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