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

DOI

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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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  - 10.2991/mmme-16.2016.41
ID  - Chen2016/10
ER  -