Ensemble of machine learning algorithms for intrusion detection

Ensemble-classifier is a technique that uses a combination of multiple classifiers to reach a more precise inference result than a single classifier. In this paper, a three-layer hierarchy multi-classifier intrusion detection architecture is proposed to promote the overall detection accuracy. For making every individual classifier is independent from others, each uses a diverse soft computing technique as well as different feature subset. In addition, the performances of a variety of combination methods that fuse the outputs from classifiers are studied. In the experiments, DARPA KDD99 intrusion detection data set is chosen as the evaluation tools. The results show that our approach achieves a better performance than that of a single classifier.