Fuzzy Min-Max Classifier in Cybersecurity Applications

Abstract

A modified fuzzy min-max classifier is presented that differs from the original in the way that the hyperbox expansion operation is performed. The classifier has been tested on the solution of cybersecurity problems, such as detecting spam, phishing sites and attacks on network connections. The results of experiments results showed an improvement in the accuracy relative to the original fuzzy min-max classifier. Comparisons with six alternative incremental learning classifiers showed competitive results on the false acceptance rate, the false reject rate, and the F1-score values.