AI based supervised classifiers: an analysis for intrusion detection

Abstract

Researchers investigated Artificial Intelligence (AI) based classifiers for intrusion detection to cope the weaknesses of knowledge based systems. AI based classifiers can be utilized in supervised and unsupervised mode. Here, we perform a blind set of experiments to compare & evaluate performance of the supervised classifiers by their categories using variety of metrics. The performance of the classifiers is analyzed using subset of benchmarked KDD cup 1999 dataset as training & Test dataset. This work has significant aspect of using variety of performance metrics to evaluate the supervised classifiers because some classifiers are designed to optimize some specific metric. This empirical analysis is not only a comparison of various classifiers to identify best classifier on the whole and best classifiers for individual attack classes, but also reveals guidelines for researchers to apply AI based classifiers to field of intrusion detection and directions for further research in this field.