Intrusion Detection Technology based on CEGA-SVM

Yuxin Wei; Muqing Wu

3rd International ICST Conference on Security and Privacy in Communication Networks

Research Article

Intrusion Detection Technology based on CEGA-SVM

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1109/SECCOM.2007.4550339,
    author={Yuxin Wei and Muqing Wu},
    title={Intrusion Detection Technology based on CEGA-SVM},
    proceedings={3rd International ICST Conference on Security and Privacy in Communication Networks},
    publisher={IEEE},
    proceedings_a={SECURECOMM},
    year={2008},
    month={6},
    keywords={conditional entropy  genetic algorithm  intrusion detection  optimal feature subset  support vector machine},
    doi={10.1109/SECCOM.2007.4550339}
}

Yuxin Wei
Muqing Wu
Year: 2008
Intrusion Detection Technology based on CEGA-SVM
SECURECOMM
IEEE
DOI: 10.1109/SECCOM.2007.4550339

Yuxin Wei¹^,*, Muqing Wu¹^,*

1: Institute of Communication Networks Integrated Technique BUPT, Beijing, China

*Contact email: weiyuxin@gmail.com, wumuqing@bupt.edu.cn

Abstract

In order to improve the classification accuracy and reduce the detection time, the optimization of feature extraction and SVM training model is combined together. In the procedure of feature extraction using CEGA with adaptive crossover and mutation, fitness of the individual is evaluated by the correct classification rate and conditional entropy. The optimization of SVM training model is processed at the same time with the feature extraction in order to find the best combination of optimal feature subset with the SVM training model. Results of the experiment using KDD CUP99 data sets demonstrate that applying CEGA-SVM can be an effective way for feature extraction and intrusion detection.

Keywords: conditional entropy genetic algorithm intrusion detection optimal feature subset support vector machine

Published: 2008-06-24
Publisher: IEEE
Modified: 2011-08-03

: http://dx.doi.org/10.1109/SECCOM.2007.4550339