Abstract: The traditional classification algorithms always have low classification accuracy rate especially for the minorityclass when they are directly employed on classifying imbalanced datasets．A K-S statistic based new classification method for imbalanced data was proposed to enhance the performance of minority class recognition．At first,the K-S statistic was employed as a correlation measure to remove redundant variables．Then a K-S based decision tree was built to segment the training data into several subsets．Finally,two-way resampling methods,forward and backward,were used to rebuild the segmentation datasets as to implement more reasonable classification learning．The proposed K-S based method,with a realistic assumption,is very high efficient and widely applicable．The KDD99intrusion detection experimental analysis proves that the method has high classification accuracy rate of both minority and majority class for imbalanced datasets.

Key words: Imbalanced data,K-S statistic,Logistic regression,Intrusion detection