Intrusion Detection Method Based on Kernel Density Estimator
Liu Liu · Jisuanji gongcheng · 2008
This paper analyses the disadvantages of the existing intrusion detection technology and discusses the advantages of intrusion detection based on outlier mining,a new intrusion detection method based on kernel density estimator called IDKD is proposed.In IDKD,the approximate set of outliers is calculated by kernel density estimator through one data set pass,and the indeed set of outliers is generated from the approximate set by another data set pass,the anomaly records are detected.This method is applied in KDD99 data set and gets satisfactory results.