Increase the Speed of Detection and Recognition of Computer Attacks in Combined Diagonalized Neural Networks

被引：0

作者：

Lytvyn, Vasyl ^{[1
]}

Peleshchak, Ivan ^{[1
]}

Peleshchak, Roman ^{[2
]}

机构：

[1] Lviv Polytech Natl Univ, Informat Syst & Networks Dept, Lvov, Ukraine

[2] Ivan Franko Drohobych State Pedag Univ, Dept Gen Phys, Drogobych, Ukraine

来源：

2017 4TH INTERNATIONAL SCIENTIFIC-PRACTICAL CONFERENCE PROBLEMS OF INFOCOMMUNICATIONS-SCIENCE AND TECHNOLOGY (PIC S&T) | 2017年

关键词：

recirculation neural network; multilayer perceptron; a matrix of' weight connections; diagonalization matrix; eigenvalues; eigenvectors; a computer attack;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a method increase the speed of detection and recognition of computer attacks using diagonalization matrices of synaptic connections in series connected recirculating neural network and multilayer perceptron. It is established that the decrease in the number of weight of synaptic connections between neurons reduces the vulnerability of the combined neural networks of various computer attacks by about 10% The main advantages of this approach is the ability of the combined diagonalized the neural network to reduce the time of adaptation to dynamic conditions and to increase the speed of its functioning in real time.

引用

页码：152 / 155

页数：4

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