E3S Web Conf.
Volume 353, 20228th International Conference on Energy and City of the Future (EVF’2021)
|Number of page(s)||14|
|Section||Materials & Factories of the Future|
|Published online||29 June 2022|
Hybrid Threats against Industry 4.0: Adversarial Training of Resilience
1 Faculty of Information Technology, University of Jyväskylä, PO Box 35, FI-40014, Jyväskylä, Finland
2 Department of Artificial Intelligence, Kharkiv National University of Radio Electronics, Nauky Ave. 14, 61166, Kharkiv, Ukraine
* Corresponding author: firstname.lastname@example.org
Industry 4.0 and Smart Manufacturing are associated with the Cyber-Physical-Social Systems populated and controlled by the Collective Intelligence (human and artificial). They are an important component of Critical Infrastructure and they are essential for the functioning of a society and economy. Hybrid Threats nowadays target critical infrastructure and particularly vulnerabilities associated with both human and artificial intelligence. This article summarizes some latest studies of WARN: “Academic Response to Hybrid Threats” (the Erasmus+ project), which aim for the resilience (regarding hybrid threats) of various Industry 4.0 architectures and, especially, of the human and artificial decision-making within Industry 4.0 processes. This study discovered certain analogy between (cognitive) resilience of human and artificial intelligence against cognitive hacks (special adversarial hybrid activity) and suggested the approaches to train the resilience with the special adversarial training techniques. The study also provides the recommendations for higher education institutions on adding such training and related courses to their various programs. The specifics of related courses would be as follows: their learning objectives and related intended learning outcomes are not an update of personal knowledge, skills, beliefs or values (traditional outcomes) but the robustness and resilience of the already available ones.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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