Network security analysis of weighted neural network with association rules mining

DOI

10.2991/mmebc-16.2016.421How to use a DOI?

Keywords

Intrusion classification; mark; security; network; neural network

Abstract

This article applies Co-S3OM semi-supervised learning algorithm to intrusion detection field and proposes specific semi-supervised network intrusion classification scheme. In accordance with different type of attack, different mark samples are selected as training set to complete initialization of three S3OM classifiers; marked sample data is expanded with coordinative vote by three classifiers. Test structure process is given in detail to use KDD Cup 99 data set to perform semi-supervised classification. It shows in test that intrusion classification model based on Co-S3OM is of high data sample marking rate and high intrusion classification rate.

Copyright

© 2016, 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  - Ziqiao Wang
AU  - Weinan Fu
PY  - 2016/06
DA  - 2016/06
TI  - Network security analysis of weighted neural network with association rules mining
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 2102
EP  - 2106
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.421
DO  - 10.2991/mmebc-16.2016.421
ID  - Wang2016/06
ER  -