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A Trusted Connection Authentication Reinforced by Bayes Algorithm

机译:贝叶斯算法增强的可信连接认证

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Trusted Connection Authentication (TCA) is a critical part of network security access solution. TCA is a kind of high level trusted network access techniques which can create trusted connections between client and remote networks through two-way user authentication and platform identification in TTP. However, there are general security problems after accessed to the network which are not much considered by existing TCA schemes. Therefore, this paper proposes a reinforced TCA architecture, TCA-BA, which extends a network behavior layer on the basis of TCA. Firstly, network behavior eigenvalue extraction is proposed by using time and host network flow characteristics. Secondly, a new method is illustrated in which we classify the behavior by Naive Bayes Algorithm, measure the network abnormal behavior by minimum risk bayes rules, identify these behaviors which have accessed to the network. Finally, the experimental results present that our architecture can effectively identify the abnormal behavior in the network and protect the network security.
机译:可信连接身份验证(TCA)是网络安全访问解决方案的关键部分。 TCA是一种高级可信网络访问技术,可以通过TTP中的双向用户身份验证和平台标识在客户端和远程网络之间创建可信连接。但是,在访问网络后,存在一些常规的安全问题,而现有的TCA方案并未对此进行过多考虑。因此,本文提出了一种增强的TCA体系结构TCA-BA,它在TCA的基础上扩展了网络行为层。首先,利用时间和主机网络流量特性,提出了网络行为特征值的提取方法。其次,说明了一种新方法,其中我们通过朴素贝叶斯算法对行为进行分类,通过最小风险贝叶斯规则测量网络异常行为,识别出已访问网络的行为。最后,实验结果表明我们的体系结构可以有效地识别网络中的异常行为并保护网络安全。

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