Numerosity reduction algorithm adapting to data classification in IDS

In order to improve the efficiency of data classification in IDS,this paper analyzed the specialties of the detected data in IDS,designed a numerosity reduction algorithm adapting to the data classification in IDS,which used range of values to reduce the amount of feature values and expand an isolated point to a region in order to forecast similar behavior.Finally,verified the validity of the designed algorithm by doing experiments with decision tree algorithms.The results of experiments show that the algorithm can reduce the time complexity and increase the classifying accuracy of the existing classification algorithms.