A hierarchy distributed-agents model for network risk evaluation based on deep learning

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 Risk Evaluation of the proposed model is built. Furthermore, to examine the proposed model, a series of experiments were conducted in terms of NSL-KDD datasets. The proposed model was able to differentiate between normal and abnormal network activities with an accuracy of 97.60% on NSL-KDD datasets. As the results acquired from the experiment indicate, the model developed in this paper is characterized by high-speed and high-accuracy processing which shall offer a preferable solution with regard to the Risk Evaluation in Network.