Deep Learning for Cyber Security Risk Assessment in IIoT Systems

Abstract

This article addresses cyber security risk assessment in industrial internet of things (IIoT) networks, and particularly the continuous risk assessment (CRA) process, which assumes real-time, dynamic risk evaluation based on the run-time data. IIoT cyber security risks, threats, and attacks are briefly presented. Requirements for cyber security risk assessment of industrial control systems as well as applicability of machine learning for that purpose are considered. The architectural view of the CRA process in the IIoT environment is presented and discussed. Possibilities of deep learning approaches to achieve CRA in IIoT systems are explored. Deep learning can be integrated into edge-computing-based systems and used for feature extraction and risk classification from massive raw data. Several research works are presented and briefly discussed. The article ends with emphasizing the future research directions and concluding remarks.