提出一种使用粗糙集与Bayes分类器的P2P网络安全管理机制.该模型放弃了局部信任度与全局信任度等概念,对不满意事件进行分类统计,对交易节点进行分类控制.创新之处有:1)通过对节点彼此之间进行交易发生的不满意事件按照交易失败的类型、损害的严重程度、交易规模的大小等情况进行分类与量化,将交易失败事件区分为恶意攻击、大规模交易且质量不满意等类型.2)使用粗糙集分类器与Bayes分类器,将对等网络中的节点划分为可信任节点、陌生节点、恶意节点等不同的类型；建立信任节点列表与恶意节点列表；交易时将恶意节点排除在外.3)建立了反馈控制机制,使用粗糙集分类器与Bayes分类器根据节点反馈推荐的意见对被评价节点进行分类、做出评价,同时监测提出评价的节点是否有恶意行为,将反馈行为划分为诚实反馈、恶意反馈等.实验表明,与已有的安全模型相比,提出的安全管理机制对恶意行为具有更高的检测率、更满意的交易成功率以及更好的反馈信息综合能力.%A new security management mechanism based on the classification control of trade nodes was proposed. The model gives over the concepts of local trust and global trust and takes the classification control of trade nodes. This paper' s innovation is as follows: 1) Through classification and quantification of failure events in trade between nodes,according to the severity of damage and the size of the trade, the trade failure events are divided into malicious attacks, bad quality and so oa 2)The rough set classifier and Bayes classifier are used and the nodes are divided into trust nodes, strange nodes and malicious nodes in a peei-to-peer network. The trust node list and the malicious node list are built. The malicious nodes are excluded from trading. 3) The rough set classifier and Bayes classifier are used to integrate the feedback recommendation and to decide the type of the recommended node. The feedback behaviors are divided into the honest feedback, the malicious feedback and so on. The experiment indicates that compared with the existing trust model , the model may obtain higher examination rate over malicious acts with the higher transaction success ratio, and has the better feedback information synthesizing capacity.
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