Open AccessApplication of an improved BP neural network algorithm in intrusion detection
Author(s) -
Xuan Chen
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 - 10.1088/1742-6596/1684/1/012086
Subject(s) - intrusion detection system , computer science , artificial neural network , forgetting , false positive rate , algorithm , artificial intelligence , operator (biology) , pattern recognition (psychology) , machine learning , philosophy , linguistics , biochemistry , chemistry , repressor , transcription factor , gene
Based on the analysis of the existing problems of BP neural network used in the detection system, on the basis of the traditional BP algorithm, the automatic variable rate learning method is adopted, the forgetting factor and random optimization operator are introduced, and they are used in the network intrusion detection system. The simulation results show that the improved BP neural network algorithm is fast and easy to converge. The algorithm effectively improves the detection rate, reduces the false positive rate and false negative rate, and has obvious advantages.