Title :
Intrusion detection technology based on CEGA-SVM
Author :
Wei, Yuxin ; Wu, Muqing
Author_Institution :
Institute of Communication Networks Integrated Technique BUPT, Beijing, China
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 :
Communication networks; Entropy; Feature extraction; Genetic algorithms; Genetic mutations; Intrusion detection; Machine learning; Neural networks; Support vector machine classification; Support vector machines; conditional entropy; genetic algorithm; intrusion detection; optimal feature subset; support vector machine;
Conference_Titel :
Security and Privacy in Communications Networks and the Workshops, 2007. SecureComm 2007. Third International Conference on
Conference_Location :
Nice, France
Print_ISBN :
978-1-4244-0974-7
Electronic_ISBN :
978-1-4244-0975-4
DOI :
10.1109/SECCOM.2007.4550339
