人类心理认知习惯与云模型相结合的P2P信任模型

摘要

To solve the problems of the computational complexity and uncertainty of trust in P2P trust model, this paper proposed a new trust model for P2P network. The proposed model evaluates the peer's trust degree from the idea that absolute priority is given to human's direct experience in human psychological cognitive habits to judge,which reduces the computational complexity and the risk of accepting false recommendation information. Further, two characteristic parameters of cloud model, entropy and hyper entropy,are used to introduce positive and negative reward factors to encourage good peers and punish malicious peers respectively. Experiments show that the proposed model can resist cheating behaviors of strategic malicious peers and also distinguish complex malicious peers which behave badly with small probabilities.%为了解决P2P网络信任模型的计算复杂度以及信任的不确定问题,提出了一种适合于P2P网络的信任模型.该模型借鉴人类心理认知习惯中优先采纳直接经验进行判断的思想来评估节点信任度,进而降低模型的计算复杂度,同时减少获取虚假推荐信息的风险.在此基础上,应用传统云模型中表征不确定性的两个参数——熵和超熵,引入奖励因子和惩罚因子分别对善意节点实施奖励和对恶意节点实施惩罚.仿真实验表明,该模型能很好地抵御网络中策略型恶意节点的欺骗行为,有效辨识出以小概率作恶的复杂恶意节点.