Intrusion detection technology based on CEGA-SVM
Yuxin Wei, Muqing Wu · 2007
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.