Abstract
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 Naïve 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.
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Xu, W., Zhang, J., Zhang, Y. (2018). A Trusted Connection Authentication Reinforced by Bayes Algorithm. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_71
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DOI: https://doi.org/10.1007/978-3-030-00563-4_71
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