Open AccessAdoption of Back Propagation Network Improved by Particle Swarm Optimization in Network Intrusion Detection
Author(s) -
Chengli Guan,
Hao Sun
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032048
Subject(s) - intrusion detection system , particle swarm optimization , computer science , artificial neural network , network security , backpropagation , data mining , intrusion , artificial intelligence , algorithm , computer network , geochemistry , geology
In order to scientifically and reasonably solve some problems in the existing intrusion detection system and effectively maintain the security of network information, the improved particle swarm optimization (PSO) algorithm and back propagation (BP) neural network are used to form a new algorithm to optimize the intrusion detection technology. First, the traditional PSO algorithm is improved, and it is used to optimize the BP neural network. Then the fused new algorithm is adopted to establish an intrusion detection model. Finally, the detection effect of the new model is verified through the experiment. The results show that the detection rate of the intrusion detection model for network attacks can be as high as 93.31%, and it also greatly reduces the false positive rate of the system, which can be as low as 1.85% and has a high practical application value. This study has important reference value for the application of BP neural network improved by PSO in network security.