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中文摘要：
声誉系统由于其聚集评价的特点,为恶意节点提供了可乘之机.一些恶意节点合谋形成GoodRep攻击组,相互虚假夸大,进而在高声誉值的掩饰下危及P2P网络安全.提出了GoodRep的攻击模型及其防御机制——RatingGuard,并给出了该机制的数学描述和模块化实现过程.RatingGuard通过分析推荐节点之间的评价行为相似度,对推荐节点进行聚类划分和异常检测,识别出存在的GoodRep攻击组节点,从而帮助声誉系统排除GoodRep攻击组的干扰.仿真结果表明,RatingGuard在GoodRep攻击组的抵制方面效果显著,有效提高了声誉系统在面对GoodRep攻击时的恶意节点检测率.
英文摘要：
Reputation systems are playing critical roles in P2P networks as a method to assess the trustworthiness of peers and to combat malicious peers. However, the characteristic of aggregating ratings makes reputation systems vulnerable to be abused by false ratings, and thus offering opportunities for malicious peers. They can conspire with each other to form a collusive clique and unfairly increase the reputation of them. Under the cover of high reputation, malicious peers can masquerade as trusted ones and violate P2P networks arbitrarily. This attack model, called GoodRep, is described in this paper. In order to defend against GoodRep attack, the RatingGuard scheme is proposed to secure P2P reputation systems. This scheme is built with three functional modules. DC, DP and AD. The data collection （DC） module supports the collection of the previous rating data among raters. The data processing （DP） module measures the rating similarity of raters~ activities by analyzing these data. To identify GoodRep cliques, the abnormal detection （AD） module detects the abnormalities through clustering partition technology. The experimental results show that our RatingGuard scheme is effective in suppressing GoodRep attack, and the reputation system with RatingGuard gains higher detection ratio of malicious peers compared with the traditional schemes.
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