A Method Based on Multiple Population Genetic Algorithm to Select Hyper-Parameters of Industrial Intrusion Detection Classifier

Abstract

The security of industrial control systems is increasingly prominent, and the performance of intrusion detection classifiers depends more on hyper-parameters. This paper proposes an improved multiple population genetic algorithm (IMPGA) used to intelligently search hyper-parameters of classifiers, and the simulated annealing algorithm (SAA) is used to control the evolution rate among various populations. In addition, the hash fitness value is used to reduce resource consumption and the directional evolution operator is introduced to optimize the population. This method can effectively avoid the algorithm falling into local optimal solution and save the optimal solution in the process of evolution. Thus, the optimal or approximate optimal combinations of hyper-parameters of classifiers are obtained and the accuracy of the classifiers is finally improved. In this paper, experiments are conducted on the following datasets: the natural gas pipeline experimental dataset of Mississippi State University from 2014 (a gas dataset), the intrusion detection systems dataset of Canadian Institute for Cybersecurity from 2017 (CICIDS2017 dataset) and an oil depot dataset. The experimental results of those three datasets show that the area under curve (AUC) of the back propagation neural network (BPNN) is more than 98%, of the extreme gradient boosting (XGBoost) is more than 99%, and of the support vector machines (SVM) is more than 98%. This selection method can effectively detect the intrusion attacks.

Keywords

• industrial control network
• intrusion detection
• genetic algorithm
• hyper-parameters optimization