A novel framework, based on fuzzy ensemble of classifiers for intrusion detection systems
Saman Masarat, Hassan Taheri, Saeed Sharifian · 2014
By developing technology and speed of communications, providing security of networks becomes a significant topic in network interactions. Intrusion Detection Systems (IDS) play important role in providing general security in the networks. The major challenges with IDSs are detection rate and cost of misclassified samples. In this paper we introduce a novel multistep framework based on machine learning techniques to create an efficient classifier. In first step, the feature selection method will implement based on gain ratio of features. Using this method can improve the performance of classifiers which are created based on this features. In classifiers combination step, we will present a novel fuzzy ensemble method. So, classifiers with more performance and lower cost have more effect to create the final classifier.