Evolutionary Deep Belief Network for Cyber-Attack Detection in Industrial Automation and Control System

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this article, we first present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the cyber-attacks of SCADA-based IACS. In PEO-DBN method, PEO algorithm is employed to determine the DBN's parameters, including number of hidden units and the size of mini-batch and learning rate, as there is no clear knowledge to set these parameters. Then, to enhance the performance of single method for cyber-attacks detection, the ensemble learning scheme is introduced for aggregation of the proposed PEO-DBN method, called EnPEO-DBN. The proposed detection methods are evaluated on gas pipeline system dataset and water storage tank system dataset from SCADA network traffic by comparing with some existing methods. Through performance analysis, simulation results show the superiority of PEO-DBN and EnPEO-DBN.

Automated summary

Industrial automation and control systems (IACS) heavily rely on supervisory control and data acquisition (SCADA) networks, which are vulnerable to cyber-attacks. This article introduces a population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect cyber-attacks in SCADA-based IACS. Ensemble learning scheme is utilized to improve detection performance, resulting in EnPEO-DBN method. Performance analysis demonstrates the superior results of PEO-DBN and EnPEO-DBN in comparison with existing methods on real datasets from SCADA network traffic.