Description |
1 online resource (240 pages) |
Contents |
Intro -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 Cyber-Physical System -- 1.2 Data-Driven Modeling -- 1.3 Side-Channel Analysis -- 1.4 Book Sections -- 1.4.1 Part I: Data-Driven Attack Modeling -- 1.4.2 Part II: Data-Driven Defense of Cyber-Physical Systems -- 1.4.3 Part III: Data-Driven Digital Twin Modeling -- 1.4.4 Part IV: Non-Euclidean Data-Driven Modeling of Cyber-Physical Systems -- 1.5 Summary -- References -- Part I Data-Driven Attack Modeling -- 2 Data-Driven Attack Modeling Using Acoustic Side-Channel -- 2.1 Introduction |
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2.1.1 Research Challenges and Contributions -- 2.2 Background and Related Work -- 2.3 Sources of Acoustic Emission -- 2.3.1 System Description -- 2.3.2 Equation of Motion -- 2.3.3 Natural Rotor Oscillation Frequency -- 2.3.4 Stator Natural Frequency -- 2.3.5 Source of Vibration -- 2.3.5.1 Electromagnetic Source -- 2.3.5.2 Mechanical Source -- 2.4 Acoustic Leakage Analysis -- 2.4.1 Side-Channel Leakage Model -- 2.4.2 Leakage Quantification -- 2.4.3 Leakage Exploitation -- 2.5 Attack Model Description -- 2.5.1 Attack Model -- 2.5.2 Components of the Attack Model -- 2.5.2.1 Data Acquisition |
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2.5.2.2 Noise Filtering -- 2.5.2.3 Maximal Overlap Discrete Wavelet Transform and Multiresolution Analysis -- 2.5.2.4 Feature Extraction -- 2.5.2.5 Regression Model -- 2.5.2.6 Classification Model -- 2.5.2.7 Direction Prediction Model -- 2.5.2.8 Model Reconstruction -- 2.5.2.9 Post-Processing for Model Reconstruction -- 2.5.3 Attack Model Training and Evaluation -- 2.6 Results for Test Objects -- 2.6.1 Speed of Printing -- 2.6.2 The Dimension of the Object -- 2.6.3 The Complexity of the Object -- 2.6.4 Reconstruction of a Square -- 2.6.5 Reconstruction of a Triangle |
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2.6.6 Case Study: Outline of a Key -- 2.7 Discussion -- 2.7.1 Technology Variation -- 2.7.2 Sensor Position -- 2.7.3 Sensor Number -- 2.7.4 Dynamic Window -- 2.7.5 Feature Separation during Multiple Axis Movement and Noise -- 2.7.6 Target Machine Degradation -- 2.8 Summary -- References -- 3 Aiding Data-Driven Attack Model with a Compiler Modification -- 3.1 Introduction -- 3.2 Attack Model Description -- 3.3 Compiler Attack -- 3.3.1 Profiling Phase -- 3.3.2 Attack Phase -- 3.3.3 Compiler Modification -- 3.3.4 Transformations for Leakage Maximization -- 3.4 Experimental Results |
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3.4.1 Accuracy Metric -- 3.4.2 Mutual Information -- 3.4.3 Partial Success Rate -- 3.4.4 Total Success Rate -- 3.5 Discussion -- 3.5.1 Countermeasures -- 3.6 Summary -- References -- Part II Data-Driven Defense of Cyber-Physical Systems -- 4 Data-Driven Defense Through Leakage Minimization -- 4.1 Introduction -- 4.1.1 Motivation for Leakage-Aware Security Tool -- 4.1.2 Problem and Challenges -- 4.1.3 Contributions -- 4.2 System Modeling -- 4.2.1 Data-driven Leakage Modeling and Quantification -- 4.2.2 Attack Model -- 4.2.3 Formulation of Data-Driven Leakage-Aware Optimization Problem |
Summary |
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 of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS. · Provides an introduction to the data-driven modeling of cyber-physical systems (CPS), to aid in capturing the stochastic phenomenon affecting CPS; · Describes practical applications for securing the CPS as well as building the digital twin of the physical twin of CPS; · Includes coverage of machine learning and artificial intelligence algorithms for data-driven modeling of the CPS; Provides novel algorithms for handling not just Euclidean data, but also non-Euclidean data |
Notes |
4.2.3.1 Design Variables for Leakage Minimization |
Bibliography |
Includes bibliographical references and index |
Notes |
Print version record |
Subject |
Cooperating objects (Computer systems)
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Cooperating objects (Computer systems)
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Form |
Electronic book
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Author |
Al Faruque, Mohammad Abdullah
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ISBN |
9783030379629 |
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3030379620 |
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