Improving network intrusion detection by identifying effective features based on probabilistic dependency trees and evolutionary algorithm

摘要

Abstract With the expansion of computer networks, attacks and intrusions into these networks have also increased. In order to achieve complete security in a computer system, in addition to firewalls and other intrusion prevention equipment, other systems called intrusion detection systems (IDS) are also required. This paper presents a new method for selecting effective features on network intrusion detection based on the distribution estimation algorithm which employs the probability dependency tree to identify the interactions between the features. To evaluate the performance of this algorithm, a new dataset was used where the packets were divided into normal categories. The performance of the proposed algorithm was investigated alone and in combination with other feature selection algorithms, including leading selection, regression selection, random forest, and genetic algorithm. The effect of algorithm parameters, such as population size was explored on the accuracy of intrusion detection. According to the results of this analysis as well as combining the results of accuracy examination within the categories obtained using different feature selection algorithms, a subset of effective features in intrusion detection was identified.