Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering

A Feature Selection Method for Anomaly Detection Based on Improved Genetic Algorithm

Authors
Shi Chen, Zhiping Huang, Zhen Zuo, Xiaojun Guo
Corresponding Author
Shi Chen
Available Online October 2016.
DOI
https://doi.org/10.2991/mmme-16.2016.41How to use a DOI?
Keywords
Anomaly detection; feature selection; genetic algorithm
Abstract

Since anomaly detection systems often need to handle large amounts of data, feature selection, which is an ef-fective method for reducing data complexity, is usually applied for anomaly detection. In this paper, an im-proved genetic algorithm based feature selection method is proposed to obtain optimal features subset with not only considering the performance of classifier but the features generation costs. An optimal weighted nearest neighbor classifier is also adopted to improve the detection performance with the selected features. The experiment results on NSL-KDD dataset show that the proposed method achieves a better or similar per-formance with 99.66% detection rate and 0.70% false negative rate, when compared with that based on all features.

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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Volume Title
Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-221-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/mmme-16.2016.41How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Shi Chen
AU  - Zhiping Huang
AU  - Zhen Zuo
AU  - Xiaojun Guo
PY  - 2016/10
DA  - 2016/10
TI  - A Feature Selection Method for Anomaly Detection Based on Improved Genetic Algorithm
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 186
EP  - 189
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.41
DO  - https://doi.org/10.2991/mmme-16.2016.41
ID  - Chen2016/10
ER  -