A network security situation prediction model based on wavelet neural network with optimized parameters Article Swipe

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Haibo Zhang , Qing Huang , Fangwei Li , Jiang Zhu ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.1016/j.dcan.2016.06.003

The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN) with optimized parameters by the Improved Niche Genetic Algorithm (INGA). The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA) so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) and WNN.

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Artificial Intelligence

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Computer science Genetic algorithm Artificial neural network Premature convergence Convergence (economics) Data mining Cluster analysis Artificial intelligence Algorithm Machine learning Economics Economic growth

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.dcan.2016.06.003
OA Status: diamond
Cited By: 40
References: 33
Related Works: 10
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DOI: https://doi.org/10.1016/j.dcan.2016.06.003

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Title: A network security situation prediction model based on wavelet neural network with optimized parameters

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Language: en

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Publication year: 2016

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Publication date: 2016-07-10

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Authors: Haibo Zhang, Qing Huang, Fangwei Li, Jiang Zhu

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Landing page: https://doi.org/10.1016/j.dcan.2016.06.003

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OA URL: https://doi.org/10.1016/j.dcan.2016.06.003

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Concepts: Computer science, Genetic algorithm, Artificial neural network, Premature convergence, Convergence (economics), Data mining, Cluster analysis, Artificial intelligence, Algorithm, Machine learning, Economics, Economic growth

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Cited by: 40

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