文章基本信息

标题：Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model
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作者：Benyou Wang ; Li Gu
期刊名称：Public Policy And Administration
印刷版ISSN：2029-2872
出版年度：2019
卷号：48
期号：4
页码：618-625
DOI：10.5755/j01.itc.48.4.24036
出版社：Kaunas University of Technology
摘要：With the popularity of the Internet, people's lives are becoming more and more convenient. However, the network security problems are becoming increasingly serious. This paper, aiming to better protect users’ network security from the internal and external malicious attacks, briefly introduces the probabilistic neural network and principal component analysis method, and combines them for detection of network intrusion data. Simulation analysis of Probabilistic Neural Network (PNN) and Principal Component Analysis-Probabilistic Neural Network (PCA-PNN) are carried out in MATLAB software. The results suggest that the Principal Component Analysis (PCA) algorithm greatly reduce the dimension of the original data and the amount of calculation. Compared with PNN, PCA-PNN has higher accuracy and precision rate, lower false alarm rate, and faster detecting speed. Moreover, PCA-PNN has better detecting performance when there are few training samples. In summary, PCA-PNN can be used for the detection of network intrusion threat..
关键词：Probabilistic Neural Network; network security; Principal Component Analysis; detection algorithm.