Data Collection for Intrusion Detection System Based on Stratified Random Sampling

Abstract

Data collection mechanism is a crucial factor of the performance of intrusion detection system (IDS). Stratified random sampling technique of statistics is introduced to the procedure of data collection of IDS, and a new data collection model and its implementation for IDS are provided in this paper. The issue of sample size allocation in strata is discussed, and formulas used to calculate the sample size of packets based on proportional allocation are presented. Experimental results show this method is able to improve the efficiency of data collection for IDS with little devaluation of detection precision, and exceedingly strengthen the processing performance of IDS especially in the large-scale high-speed network.