%0 Journal Article
	%A Anita Khadka and  Gregory Epiphaniou and  Carsten Maple
	%D 2023
	%J International Journal of Nuclear and Quantum Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 193, 2023
	%T Resilient Machine Learning in the Nuclear Industry: Crack Detection as a Case Study
	%U https://publications.waset.org/pdf/10012926
	%V 193
	%X There is a dramatic surge in the adoption of Machine Learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. Artificial Intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and two defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt ML techniques without rigorous testing, since they may be vulnerable to adversarial attacks, especially in security-critical areas such as the nuclear industry. We observed that while the adopted defence methods can effectively defend against different attacks, none of them could protect against all five adversarial attacks entirely.
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