Study on network security situation awareness based on particle swarm optimization algorithm. (November 2018)

• Record Type: Journal Article
• Authors: Zhao, Dongmei; Liu, Jinxing
• Is Part Of: Computers & industrial engineering. Volume 125(2018)
• Page Start: 764
• Page End: 775
• Publication Date: 2018-11
• Subjects: Network security situation -- Factor extraction -- Attribute importance matrix -- Rough set -- Particle swarm algorithm -- Wavelet neural network; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.01.006
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A network security situation awareness index system in big data environment is established. A parallel reduction algorithm based on attribute importance matrix is proposed. The wavelet neural network parameters is optimized by particle swarm algorithm. Abstract: The research of network security situation assessment has made great progress, but with the advent of the era of big data, the results of these studies show its limitations and shortcomings, it is difficult to meet the network security under the big data environment needs. Aiming at the network security situation awareness in large data environment, the network security situation awareness index system is firstly established, and the index factors are selected and quantified, and then the situation value is calculated to construct the network security situation awareness system. For the selection and quantification of index factors, we select multi-source data in large data environment, and propose a parallel reduction algorithm based on attribute importance matrix to reduce data source data attributes. For the calculation of the situation, the traditional wavelet neural network learning method is easy to fall into the local minimum, the wavelet neural network parameters are optimized by particle swarm algorithm, and then the wavelet neural network based on particle swarm optimization is applied to calculate the situation value. The simulation results show that the algorithm has fast convergence speed and good fitting effect.