Comparative study of malware detection techniques for industrial control systems.

Reid, Deborah; Harris, Ian; Petrovski, Andrei

Abstract

Industrial Control Systems are essential to managing national critical infrastructure, yet the security of these systems historically relies on isolation. The adoption of modern software solutions, and the unique challenges presented by legacy systems, has made securing industrial networks increasingly difficult. With malware identified as the leading cause of cyber incident in industrial systems, this work presents a comparative study of existing malware detection techniques, to compare both accuracy and suitability for use in the defence of industrial systems.

Citation

REID, D., HARRIS, I. and PETROVSKI, A. 2021. Comparative study of malware detection techniques for industrial control systems. In Moradpoor, N., Elçi, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 19. Available from: https://doi.org/10.1109/SIN54109.2021.9699167

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 14th International conference on Security of information and networks 2021 (SIN 2021)
• Start Date: Dec 15, 2021
• End Date: Dec 17, 2021
• Acceptance Date: Dec 7, 2021
• Online Publication Date: Dec 17, 2021
• Publication Date: Feb 10, 2022
• Deposit Date: Feb 11, 2022
• Publicly Available Date: Feb 11, 2022
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Book Title: Proceedings of the 14th International conference on Security of information and networks 2021 (SIN 2021)
• ISBN: 9781728192666
• DOI: https://doi.org/10.1109/SIN54109.2021.9699167
• Keywords: Industrial control systems; Malware detection; Machine learning; Cybersecurity
• Public URL: https://rgu-repository.worktribe.com/output/1592334