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Poisonedwater: an adaptive approach to reducing the reputation ranking error in P2P networks

机译:中毒水:一种自适应方法,可以减少P2P网络中声誉排名误差的方法

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This paper preliminarily proposes a reputation ranking algorithm called "Poisonedwater" to resist front peer attack - peers that gain high reputation values by always cooperating with other peers and then promote their malicious friends through passing most of their reputation values to those malicious peers. Specifically, we introduce a notion of Poisoned Water (PW) that iteratively floods from identified malicious peers in the reverse direction of the incoming trust links towards other peers. Furthermore, we propose the concept of Spreading Factor (SF) that is logistically correlated to each peer's PW level. Then, we design the new reputation ranking algorithm seamlessly integrated with peers' recommendation ability {represented as SF), to infer the more accurate reputation ranking for each peer. Simulation results show that, in comparison with Eigentrust, Poisonedwater can significantly reduce the ranking error ratio up to 20%, when P2P systems exist many malicious peers and front peers.
机译:本文初步提出了一种名为“中毒水”的声誉排名算法,以抵抗前同行攻击 - 通过与其他同行共同合作,通过与其他同龄人合作,通过将大部分声誉价值传递给恶意同行,以促进他们的恶意朋友来抵抗高声誉价值的同行。具体而言,我们引入了毒液(PW)的概念,从识别的恶意同行迭代泛滥,以传入的信任链路的反向对待其他同行。此外,我们提出了与每个对等体的PW水平逻辑相关的扩展因子(SF)的概念。然后,我们设计了与对等体推荐能力无缝集成的新声誉排名算法{表示为SF),以推断每个对等体的更准确的信誉排名。仿真结果表明,与实事术相比,当P2P系统存在许多恶意同龄人和前同行时,中毒水可以显着降低高达20%的排名误差比。

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