A new intelligent intrusion detector based on ensemble of decision trees

Artificial intelligence and machine learning are in widespread use nowadays in order to develop automatic and precise models for different tasks especially in the Internet. In this paper, by the use of machine learning techniques, an intrusion detection system is proposed. An intrusion detection system is involved extensive mass of data; such data is naturally characterized with repetitions and noise which leads to the reduction in the stability and the accuracy of the intrusion detection system. Hence, the issue of reducing features dimensions for achieving a smaller subset of features which can precisely express the results and status of network observations has attracted a lot of researchers' attention. In the proposed method, by using gradually feature removal method, 16 critical features were selected for representing various network visits. By combining ant colony algorithm and ensemble of decision trees, we proposed an efficient and stable classifier for judging a network visit to be normal or not. Despite the selection of 16 features, high accuracy, i.e. 99.92%, and the average value of Matthews correlation coefficient 0.91 are obtained.