Technique for Intrusion Detection Based on NSST Domain Artificial Neural Networks
Lei Yang, Jing Ma · Applied Mechanics and Materials · 2015
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.