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    P2P信任模型中基于模糊逻辑的共谋团体识别方法

    A Collusion Detector Based on Fuzzy Logic in P2P Trust Model

    • 摘要: 共谋团体已成为P2P信任模型所面临的首要威胁,但是现有信任模型普遍缺乏针对共谋团体的有效识别和防范机制,使信任模型的安全性受到严重威胁.在充分考虑节点评分模糊性和共谋行为特征的基础上提出了一种基于模糊逻辑的共谋团体识别方法,该方法通过对信任模型中节点的评分行为进行分析,将语言变量、模糊逻辑引入到共谋团体识别过程中,能够更加有效地识别节点之间的共谋行为.实验表明,FDC模型在增强信任模型可靠性方面效果显著.

       

      Abstract: As peer-to-peer (P2P) applications have achieved enormous success, the security of P2P network is getting more and more concerns. Particularly, the trust problem has become bottleneck because of the open and anonymous nature of P2P network. Trust model provides a security mechanism for P2P network and is proven to play an important role in improving the robustness of P2P network. However, colluding peers pose a serious threat to the security of trust model. For example, colluding peers may subvert the trust model by spreading plenty of false evaluation. Most existing trust models in P2P network are known as completely green hand in handling colluding peers. Some of them pose measures to make colluding attack difficult, but they are far from satisfying mainly because they cannot identify the colluding peers in P2P network. To address the problem, a fuzzy detector of collusion (FDC) is proposed to detect the existence of colluding peers by measuring similarity of peer's behavior. We show how to deploy FDC in distributed P2P network. The simulation results presented show that FDC is considerably effective in detecting and thwarting collusion attack and FDC can help the trust system in P2P network resist threats from colluding attackers.

       

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