Abstract
The analysis of statistical sensitivity for training neural networks using a new kind of orthogonal weight functions (OWFs) is discussed in this paper. The weights obtained after training are orthogonal functions defined on the sets of input variables (input patterns). We design a kind of classifier for intrusion detection. By extracting some parameters using the sensitivity formula of OWFs neural networks given in this paper, the test data for intrusion detection are optimized. We show that the classifier of OWFs neural networks has the advantages of optimized architecture and high detection rate.
Highlights
To overcomes the drawbacks that usually suffered from early algorithms (BP, RBF), a new type of artificial neural network’s training algorithm using cubic spline weight functions (CSWFs) has been proposed in [1]
Based on [1], a new algorithm using orthogonal weight functions (OWFs) is introduced in this paper for the implementation of weight functions for training neural networks, so it has the advantages of CSWFs [1]
It is well known that sensitivity refers to how a system output is influenced by its input perturbations, and sensitivity analysis is very closely related with the architecture of neural networks and the training algorithms
Summary
To overcomes the drawbacks that usually suffered from early algorithms (BP, RBF), a new type of artificial neural network’s training algorithm using cubic spline weight functions (CSWFs) has been proposed in [1]. Detection method that based on neural networks determines the invasion by extracting the mode characteristics from the normal or abnormal behaviors of the user or the systems, and creates the outline of their behavioral characteristics, according to the normal or abnormal outline during the intrusion detection to judge the exception of audit data. It can constantly learning and adjust the Mode characteristics of the subject by training, in order to build a characteristics outline which is adaptive.
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