Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry
Anita Khadka,
Saurav Sthapit,
Gregory Epiphaniou
et al.
Abstract:The widespread adoption and success of Machine Learning (ML) technologies depend on thorough testing of the resilience and robustness to adversarial attacks. The testing should focus on both the model and the data. It is necessary to build robust and resilient systems to withstand disruptions and remain functional despite the action of adversaries, specifically in the security-sensitive industry like the Nuclear Industry (NI) where consequences can be fatal in terms of both human lives and assets. We analyse M… Show more
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