Comparative Study of Supervised Machine Learning Techniques for Intrusion Detection

Abstract

Intrusion detection is an effective approach for dealing with various problems in the area of network security. This paper presents a comparative study of using supervised probabilistic and predictive machine learning techniques for intrusion detection. Two probabilistic techniques Naive Bayes and Gaussian and two predictive techniques decision tree and random forests are employed. Different training datasets constructed from the KDD99 dataset are employed for training. The ability of each technique for detecting four attack categories (DoS,Probe,R2L and U2R) have been compared. The statistical results to show the sensitivity of each technique to the population of attacks in a dataset have also been reported. We compare the performance of the techniques and also investigate the robustness of each technique by calculating their standard deviations with respect to the detection rate of each attack category.