Title: Research on network intrusion detection security based on improved extreme learning algorithms and neural network algorithms
Authors: Zhenjun Dai
Addresses: Hunan Communication Polytechnic, Hunan, Changsha 410132, China
Abstract: In order to improve the ability of network fuzzy intrusion detection, a network intrusion detection method based on improved extreme learning algorithm and neural network algorithm is proposed to improve the security of the network. ARMA and other linear detection methods are used to construct the network intrusion signal model, and the nonlinear time series and chaos analysis methods are used to extract the feature of network intrusion and big data information analysis. The limit learning method is used for active detection of network intrusion; the adaptive learning method is used for iterative analysis of network intrusion detection, and the correlation characteristic decomposition method is used to improve the convergence of network intrusion detection. The fuzzy neural network algorithm is used to classify the network intrusion features to improve the intrusion detection performance. The simulation results show that this method has high accuracy and strong anti-jamming ability; it has good application value in network security.
Keywords: extreme learning; network intrusion; neural network algorithm; detection; nonlinear time series analysis.
International Journal of Biometrics, 2020 Vol.12 No.1, pp.56 - 66
Received: 02 Mar 2019
Accepted: 11 Apr 2019
Published online: 26 Feb 2020 *