Industrial Internet of Things Security Modelling using Ontological Methods

Aslam Jarwar, Muhammad; Watson, Jeremy; Ani, Uchenna; Chalmers, Stuart

Abstract

The Industrial Internet of Things (IIoT) trend presents many significant benefits for improving industrial operations. However, its emergence from the convergence of legacy Industrial Control Systems (ICS) and Information and Communication Technologies (ICT) has introduced newer security issues such as weak or lack of end-to-end security. These challenges have weakened the interest of many critical infrastructure industries in adopting IIoT-enabled systems. Implementing security in IIoT is challenging because this involves many heterogeneous Information Technology (IT) and Operational Technology (OT) devices and complex interactions with humans, and the environments in which these are operated and monitored. This article presents the initial results of the PETRAS Secure Ontologies for Internet of Things Systems (SOfIoTS) project, which consists of key security concepts and a modular design of a base security ontology, which supports security knowledge representation and analysis of IIoT security.

Citation

Aslam Jarwar, M., Watson, J., Ani, U., & Chalmers, S. (2022, November). Industrial Internet of Things Security Modelling using Ontological Methods. Presented at IoT 2022: The 12th International Conference on the Internet of Things, Delft Netherlands

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IoT 2022: The 12th International Conference on the Internet of Things
• Start Date: Nov 7, 2022
• End Date: Nov 10, 2022
• Acceptance Date: Nov 5, 2022
• Publication Date: Jan 5, 2023
• Publisher: Association for Computing Machinery (ACM)
• Pages: 163 - 170
• Series Title: IoT 2022: The 12th International Conference on the Internet of Things
• Book Title: IoT '22: Proceedings of the 12th International Conference on the Internet of Things
• ISBN: 978-1-4503-9665-3
• DOI: https://doi.org/10.1145/3567445.3571103
• Public URL: https://keele-repository.worktribe.com/output/425370
• Publisher URL: https://dl.acm.org/doi/10.1145/3567445.3571103