Differential evolution-based convolutional neural networks: An automatic architecture design method for intrusion detection in industrial control systems

Abstract

Industrial control systems (ICSs) are facing serious and evolving security threats because of a variety of malicious attacks. Deep learning-based intrusion detection systems (IDSs) have been widely considered as one of promising security solutions for ICSs, but these deep neural networks for IDSs in ICSs have been designed manually, which are extremely dependent on expert experience with numerous model parameters. This paper makes the first attempt to develop an automatic architecture design method of convolutional neural networks (CNNs) based on differential evolution (abbreviated as DE-CNN) for the intrusion detection issue in ICSs. The first phase of the proposed DE-CNN is the off-line architecture optimization of the CNNs constructed by three basic units such as ResNetBlockUnit, DenseNetBlockUnit, and PoolingUnit, including encoding the architecture parameters of a CNN as a population, evaluating the fitness of the population by the validation accuracy and the number of CNN model parameters, implementing the evolutionary process including mutation and crossover operations, and selecting the best individual from the population. Then, the optimal CNN model obtained by the off-line optimization of DE-CNN is deployed for the online IDSs. The experimental results on two intrusion detection datasets in ICSs including SWaT and WADI have demonstrated the superiority of the proposed DE-CNN to the state-of-the-art manually-designed and neuroevolution-based methods under both unsupervised and supervised learning in terms of Precision, Recall, F1-Score and the number of the CNN model parameters.