A Reputation System that Resists a Collusive Attack Focused on a Specific Target

Abstract

The rapid growth of communication networks, such as the Internet and mobile networks, has spurred the development of numerous online trading systems. It is important for such a community that there be trust between the trading partners. A system that stores information about the reputation for trustworthiness of the individuals in the network is known as a reputation system. However, a malicious evaluator can provide an incorrect evaluation, and several such users may collude to create a false evaluation of a particular target; this is known as a pinpoint collusive attack and is a major problem. In order to resist such an attack, reputation systems have been proposed that consider the ability of a given evaluator to provide an accurate evaluation of a given target. However, these systems are unable to identify attacks from evaluators who have achieved a sufficiently high rating for their ability. In this paper, we propose a novel algorithm that, when calculating the ability of an evaluator, reduces the influence of values that are not in line with the daily trend. Simulation results show that the proposed method reduces the influence of an attack.