The Comparison of the Relative Entropy for Intrusion Detection on CPU and GPU

Abstract

When analyzing the behavior pattern of the object that is composed of a set of various samples, it is an efficient way to analyze its pattern based on the features’ probability distribution among a set of samples. Contrast to the traditional ways that focus on each sample’s feature patterns and then build a model to differentiate this one from another one, the probability distribution based pattern recognition can conclude a set of samples' features by analyzing its sample probability distribution model and then differentiate this set of samples from another one. By this way, not only can we save a lot of time and resource, but also it is more representative to display the group features of a set of samples. This paper makes use of this special advantage of the probability distribution based pattern recognition to detect the network anomalies by analyzing the different guideline such as Probability Distribution, Relative Entropy and Normalized Relative Entropy. In addition, this paper also analyzes the efficiency among the different algorithm implementations, CPU based serial algorithm, GPU based parallel algorithm and GPU based MapReduce parallel algorithms.