The Effect of Feature Selection on Detection Accuracy of Machine Learning Algorithms
Noureldien A. Noureldien, Raghda A. Hussain, Khalid Ahmed · 2013
Machine learning algorithms are commonly used to detect anomalies in network traffic. Recently, many research studies are focus on the detection performance of classification algorithms. Determining the optimistic performance of an algorithm is dependent on various factors and determining the optimistic detection performance for a given algorithm is a challenging research problem. In this paper an experiment was conducted to see the effect of feature selection on the detection performance of machine learning algorithms. The algorithms Trees.J48, Bayes.BayesNet, Functions.Logistic, Meta.Bagging and Rules.ZeroR are used to test their detection performance of DoS attacks in KDDCup99 data set using different sets of features. The experimental results show that an algorithm detection performance is dependent on the selected features and the general detection behavior is independent of the number of selected features. 1.