Authors:
Janno Jaal
1
;
2
and
Hayretdin Bahsi
1
;
3
Affiliations:
1
Department of Software Science, Tallinn University of Technology, Tallinn, Estonia
;
2
Cybernetica AS, Tallinn, Estonia
;
3
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, U.S.A.
Keyword(s):
Healthcare, Machine Learning, Adversarial Attacks, Cyber Threats, Threat Modeling.
Abstract:
Considering the immense pace in machine learning (ML) technology and related products, it may be difficult to imagine a software system, including healthcare systems, without any subsystem containing an ML model in the near future. However, ensuring the resiliency of these ML-based systems against cyber attacks is vital for more seamless and widespread technology usage. The secure-by-design principle, considering security from the early stages of development, is a cornerstone to achieving sufficient security at a reasonable cost. The realization of this principle starts with conducting threat modeling to understand the relevant security posture and identify cyber security requirements before system design. Although threat modeling of software systems is widely known, it is unclear how to apply it to software systems with machine learning models. Although adversarial machine learning is a widely studied research topic, it has yet to be thoroughly researched how adversarial and convent
ional cybersecurity attacks can be holistically considered to identify applicable cyber threats at the early stage of a software development life cycle. This paper adapts STRIDE, a widely-known threat modeling method, for the holistic cyber threat analysis of an ML-based healthcare system.
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