Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model

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.