Research on Network Intrusion Detection Technology Based on Data Mining Technology

DOI

10.2991/icmii-15.2015.77How to use a DOI?

Keywords

Data mining, BP neural network, Network intrusion detection, particle swarm optimization algorithm.

Abstract

In this paper, the technology of network intrusion detection based on data mining technology is studied. As the conventional BP neural network be used to establish the network intrusion detection techniques has some problems, because the BP neural network is easy to fall into minimum value and the accuracy is low, the paper uses particle swarm algorithm to optimize the BP neural network model, and uses dynamic inertia weight coefficient to determine the parameters of BP neural network. By using dynamic inertia weight coefficient to determine the parameters of BP neural network, by combining the network intrusion traffic characteristics and BP neural network parameters to encode to a particle, we achieved the parameters of the network intrusion traffic characteristics and BP neural network synchronization selection. By using the KDD CUP99 database of intrusion traffic data to train and test the model we proposed and the conventional model separately, the results show that the algorithm we proposed has better detection efficiency and detection accuracy.

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  - Lijun Zhou
AU  - Hong Lv
AU  - Yuan Zhao
PY  - 2015/10
DA  - 2015/10
TI  - Research on Network Intrusion Detection Technology Based on Data Mining Technology
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 444
EP  - 450
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.77
DO  - 10.2991/icmii-15.2015.77
ID  - Zhou2015/10
ER  -