detection technology for underlying intrusion of large embedded network

DOI

10.2991/iccset-14.2015.58How to use a DOI?

Keywords

rough set theory; neural network; Attribute Reduction; intrusion detection;

Abstract

The underlying intrusion accurate detection of large embedded network is studied. For the problem that low accuracy of underlying intrusion detection for the large embedded network, an underlying intrusion detection method for the large embedded network based on field rough set theory and BP neural network algorithm is proposed. Firstly, the concept of field is introduced on the basis of rough set theory to reduce the loss of information, field rough set theory is utilized to simplify data, the simplified data set are regarded as the input data of BP neural network, so as to simplify the structure of BP neural network, while reducing the training time of a sample, and improve the classification accuracy of BP neural network. The simulation results in Matlab show that the proposed algorithm for intrusion detection can achieve shorter training time for samples, while the false alarm rate of the invasion has been improved to meet the underlying intrusion detection needs of a large embedded network.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yimei Lai
PY  - 2015/01
DA  - 2015/01
TI  - detection technology for underlying intrusion of large embedded network
BT  - Proceedings of the 2014 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SP  - 270
EP  - 273
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccset-14.2015.58
DO  - 10.2991/iccset-14.2015.58
ID  - Lai2015/01
ER  -