Technique for Intrusion Detection Based on NSST Domain Artificial Neural Networks

Yang Lei; Jing Ma

doi:10.4028/www.scientific.net/AMM.713-715.2519

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715Technique for Intrusion Detection Based on NSST...

Technique for Intrusion Detection Based on NSST Domain Artificial Neural Networks

Abstract:

The issue of intrusion detection has been an active topic in both military and civilian areas, and a great many relevant algorithms and techniques have been developed accordingly. This paper addresses a novel technique based on non-subsampled shearlet transform (NSST) domain artificial neural networks (ANN) to solve the above problem, employing multi-scale geometry analysis (MGA) of NSST and the train characteristics of ANN together. Experimental results indicate that, compared with other existing conventional intrusion detection tools, the proposed one is superior to other current popular ones in both aspects of iteration numbers and convergence rates.

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Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

2519-2522

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.2519

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Online since:

January 2015

Authors:

Yang Lei*, Jing Ma

Keywords:

Artificial Neural Network (ANN), Intrusion Detection, Non-Subsampled Shearlet Transform

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* - Corresponding Author

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