Feature selection of intrusion detection data using a hybrid genetic algorithm/KNN approach
Melanie Middlemiss, Grant Dick · Hybrid Intelligent Systems · 2003
Feature selection is an important part of the information processing and system development process. The selection of an appropriate set of features can provide an insight into the underlying processes present in the data set and greatly improve the accuracy of the overall classification model. In this paper we investigate the use of a hybrid genetic algorithm/k-nearest neighbour approach to features selection and apply this approach to an intrusion detection data set. We have found that this feature selection process is able to identify features that are important for identifying different types of attacks present in the, data set leading to improved classification accuracy.