An Intrusion Detection Model Based on Non-Negative Matrix Factorization

Shan Xiong Chen; Sheng Wu; Yi Cao; Dong Sheng Tang

doi:10.4028/www.scientific.net/AMM.148-149.895

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 148-149An Intrusion Detection Model Based on Non-Negative...

An Intrusion Detection Model Based on Non-Negative Matrix Factorization

Abstract:

In this paper ,we discus a wide class of loss functions for non-negative matrix factorization (NMF) and derive flexible and improved NMF algorithms based alpha-divergences for error measures, which generalize or combine several different criteria in order to extract intrusion signal.

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Applied Mechanics and Materials (Volumes 148-149)

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895-899

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https://doi.org/10.4028/www.scientific.net/AMM.148-149.895

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Online since:

December 2011

Authors:

Shan Xiong Chen, Sheng Wu, Yi Cao, Dong Sheng Tang

Keywords:

Alpha Divergence, Intrusion Detection, Non-Negative Matrix Factorization

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References

[1] Liu, W.X., K.H. Yuan, and D.T. Ye, Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis. Journal of Biomedical Informatics, 2008. 41(4): pp.602-606.

DOI: 10.1016/j.jbi.2007.12.003

Google Scholar

[2] Pauca, V.P., J. Piper, and R.J. Plemmons, Nonnegative matrix factorization for spectral data analysis. Linear Algebra and Its Applications, 2006. 416(1): pp.29-47.

DOI: 10.1016/j.laa.2005.06.025

Google Scholar

[3] Cichocki, A., S. Amari, and R. Zdunek, Extended SMART algorithms for nonnegative matrix factorization. In: Proc. Eighth Internat. Conf. on Artificial Intelligence and Soft Computing, Zakopane, Poland., (2006).

DOI: 10.1007/11785231_58

Google Scholar

[4] Lin, C.J., On the convergence of multiplicative update algorithms for nonnegative matrix factorization. IEEE Trans. Neural Networks, 2007. 18: p.1589–1596.

DOI: 10.1109/tnn.2007.895831

Google Scholar

[5] Cichocki, A., R. Zdunek, and S. Amari, Csiszár's divergences for non-negative matrix factorization: Family of new algorithms. In: Proc. Internat. Conf. on Independent Component Analysis and Blind Signal Separation, Charleston, South Carolina., (2006).

DOI: 10.1007/11679363_5

Google Scholar

[6] Liu, W.X., K.H. Yuan, and D.T. Ye, On alpha-divergence based nonnegative matrix factorization for clustering cancer gene expression data. Artificial Intelligence in Medicine, 2008. 44(1): pp.1-5.

DOI: 10.1016/j.artmed.2008.05.001

Google Scholar

[7] Cichocki, A., S. Cruces, and S. Amari, Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization. Entropy, 2011. 13(1): pp.134-170.

DOI: 10.3390/e13010134

Google Scholar

[8] Cichocki, A. and S. -i. Amari, Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities. Entropy, 2010. 12: pp.1532-1568.

DOI: 10.3390/e12061532

Google Scholar