Titlebook: Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis; Sujit Rokka Chhetri,Mohammad Abdullah Al Faruque Book 2020 Spr

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Aiding Data-Driven Attack Model with a Compiler Modificationigh level of stealth. We present an implementation of such a compiler here and demonstrate how it can increase the success rate of recovering G/M-codes from the four side-channels (acoustic, power, vibration, and electromagnetic) by up to 39% compared to previously proposed data-driven attack model.

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Data-Driven Defense Through Leakage Minimizationate how by changing either machine process or product design parameters in the cyber-domain, we can minimize the information leakage. The methodology presented in this chapter aids the existing cyber-domain and physical domain security solution by utilizing the cross-domain relationship.

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g CPS.Describes practical applications for securing the CPS .This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect

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Word Meaning and Word Recognitionyber 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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Problems with Linear Capacity Constraintss, govern the physical dynamics of the system. Due to the juxtaposition of cross-layer components (., etc.) and cross-domain components, CPS provides various technology solutions to multiple fields (., etc.).

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Persuasive Function of Languageto figure out if they behave as side-channels, informing about the various states of both cyber and physical domains. Then, we will present a dynamic data-driven application system enabled digital twin, which is able to check if it is the most up-to-date version of the physical twin.