DocumentCode :
2294688
Title :
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
Link To Document :
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