Towards Effective Feature Selection for IoT Botnet Attack Detection Using a Genetic Algorithm

Abstract

With the large-scale use of the Internet of Things, security issues have become increasingly prominent. The accurate detection of network attacks in the IoT environment with limited resources is a key problem that urgently needs to be solved. The intrusion detection system based on network traffic characteristics is one of the solutions for IoT security. However, the intrusion detection system has the problem of a large number of traffic features, which makes training and detection slow. Aiming at this problem, this work proposes a feature selection method based on a genetic algorithm. The experiments performed on the Bot-IoT botnet detection dataset show that this method successfully selects 6 features from the original 40 features, with a detection accuracy of 99.98% and an F1-score of 99.63%. Compared with other methods and without feature selection, this method has advantages in training time and detection accuracy.