Research on model of combining multiple neural networks by fuzzy integral-MNNF

Yue Fu; Bianfang Chai

doi:10.1117/12.2010845

14 March 2013 Research on model of combining multiple neural networks by fuzzy integral-MNNF

Yue Fu, Bianfang Chai

Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87681T (2013) https://doi.org/10.1117/12.2010845
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore

Abstract

The method of multiple neural network Fusion using Fuzzy Integral (MNNF) presented by this paper is to improve the detection performance of data mining-based intrusion detection system. The basic idea of MNNF is to mine on distinct feature training dataset by neural networks separately, and detect TCP/IP data by different neural networks, and then nonlinearly combine the results from multiple neural networks by fuzzy integral. The experiment results show that this technique is superior to single neural networks for intrusion detection in terms of classification accuracy. Compared with other combination methods such as Majority, Average, Borda count, fuzzy integral is better than one of them.

Citation Download Citation

Yue Fu and Bianfang Chai "Research on model of combining multiple neural networks by fuzzy integral-MNNF", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87681T (14 March 2013); https://doi.org/10.1117/12.2010845

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