Abstract
In this chapter, we will present a data-driven security framework for modeling the cross-domain security of cyber-physical production systems. Specifically, we will present a novel conditional generative adversarial network-based modeling approach to abstract and estimate the relations between the cyber and physical domains. Using this framework, we will demonstrate how we can determine if various security requirements such as confidentiality, availability, and integrity are met. We will analyze the proposed framework for performing a security analysis of a cyber-physical additive manufacturing system.
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Notes
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An MoC is a set of allowable operations used in computation and their respective costs (e.g., timing, performance, and memory overhead).
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Rokka Chhetri, S., Al Faruque, M.A. (2020). Data-Driven Security Analysis Using Generative Adversarial Networks. In: Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-37962-9_6
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DOI: https://doi.org/10.1007/978-3-030-37962-9_6
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