Jin Yang1, Tao Li1, Gang Liang1,*, Wenbo He2, Yue Zhao3
CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.1, pp. 1-23, 2019, DOI:10.32604/cmes.2019.04727
Abstract Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic, which is especially relevant in Network Intrusion Detection. In this paper, as enlightened by the theory of Deep Learning Neural Networks, Hierarchy Distributed-Agents Model for Network Risk Evaluation, a newly developed model, is proposed. The architecture taken on by the distributed-agents model are given, as well as the approach of analyzing network intrusion detection using Deep Learning, the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented, and the hierarchical evaluative framework for Network More >
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