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Book Cover
E-book
Author Rokka Chhetri, Sujit

Title Data-driven modeling of cyber-physical systems using side-channel analysis / Sujit Rokka Chhetri, Mohammad Abdullah Al Faruque
Published Cham : Springer, 2020

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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
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
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
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
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)
Cooperating objects (Computer systems)
Form Electronic book
Author Al Faruque, Mohammad Abdullah
ISBN 9783030379629
3030379620