Abstract

Detect attacks is a major problem in cybersecurity, due to its wide variety of types, behaviors and dynamic structure. In this paper, we include a special crossover operator, which considers the features importance, providing additional information to a wrapped evolutionary algorithm, which uses random forest as classification technique. Results show that random forest improves its classification quality, when the wrapper evolutionary algorithm includes feature importance information for feature selection.