Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Becue, A (Author) Other; Praca, I (Author) Other; João Gama (Author) FEP

Journal

Title: Artificial Intelligence Review

Vol. 54 No. 5

Pages: 3849- 3886

ISSN: 0269-2821

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 123 Citations

Scopus - 211 Citations

Other information

Authenticus ID: P-00T-D6J

DOI: 10.1007/s10462-020-09942-2

Abstract (EN): This survey paper discusses opportunities and threats of using artificial intelligence (AI) technology in the manufacturing sector with consideration for offensive and defensive uses of such technology. It starts with an introduction of Industry 4.0 concept and an understanding of AI use in this context. Then provides elements of security principles and detection techniques applied to operational technology (OT) which forms the main attack surface of manufacturing systems. As some intrusion detection systems (IDS) already involve some AI-based techniques, we focus on existing machine-learning and data-mining based techniques in use for intrusion detection. This article presents the major strengths and weaknesses of the main techniques in use. We also discuss an assessment of their relevance for application to OT, from the manufacturer point of view. Another part of the paper introduces the essential drivers and principles of Industry 4.0, providing insights on the advent of AI in manufacturing systems as well as an understanding of the new set of challenges it implies. AI-based techniques for production monitoring, optimisation and control are proposed with insights on several application cases. The related technical, operational and security challenges are discussed and an understanding of the impact of such transition on current security practices is then provided in more details. The final part of the report further develops a vision of security challenges for Industry 4.0. It addresses aspects of orchestration of distributed detection techniques, introduces an approach to adversarial/robust AI development and concludes with human-machine behaviour monitoring requirements.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 38