The Incremental Probabilistic Neural Network

Author

Kou, Jialiang ; Xiong, Shengwu ; Wan, Shuzhen ; Liu, Hongbing

Author_Institution

Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., Wuhan, China

Volume

3

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

1330

Lastpage

1333

Abstract

With the development of the Internet, the Intrusion Detection has been gradually playing a more and more important role in Network Security. Radial Basis Function Neural Network are widely used in Intrusion Detection, especially Probabilistic Neural Network. However, the detection speed is a problem which impedes it to be applied to Real-time Intrusion Detection. In this paper, for increasing the Detection Speed, the Incremental Training Method replaces the Exact Training Method. The simulation experiment shows that the detection speed of Incremental Probabilistic Neural Network is much faster than that of Exact Probabilistic Neural Network. Therefore, the Incremental Probabilistic Neural Network is more suitable for real-time intrusion detection than Exact Probabilistic Neural Network.

Keywords

Internet; probability; radial basis function networks; security of data; Internet; incremental probabilistic neural network; incremental training method; intrusion detection; network security; radial basis function neural network; Artificial neural networks; Error analysis; Intrusion detection; Neurons; Probabilistic logic; Real time systems; Training; Probabilistic neural network; exact probabilistic neural network; exact training method; incremental probabilistic neural network; incremental training method; intrusion detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583589

Filename

5583589