Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory

被引:0
|
作者
Wang, Dongxia [1 ]
Muller, Tim [1 ]
Zhang, Jie [1 ]
Liu, Yang [1 ]
机构
[1] Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore
基金
新加坡国家研究基金会;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Unfair rating attacks happen in existing trust and reputation systems, lowering the quality of the systems. There exists a formal model that measures the maximum impact of independent attackers [Wang et al., 2015] -based on information theory. We improve on these results in multiple ways: (1) we alter the methodology to be able to reason about colluding attackers as well, and (2) we extend the method to be able to measure the strength of any attacks (rather than just the strongest attack). Using (1), we identify the strongest collusion attacks, helping construct robust trust system. Using (2), we identify the strength of (classes of) attacks that we found in the literature. Based on this, we help to overcome a shortcoming of current research into collusion-resistance -specific (types of) attacks are used in simulations, disallowing direct comparisons between analyses of systems.
引用
收藏
页码:111 / 117
页数:7
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