| Description |
1 online resource (xiv, 233 pages) : illustrations (chiefly color) |
| Series |
Engineering cyber-physical systems and critical infrastructures ; volume 2 |
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Engineering cyber-physical systems and critical infrastructures ; volume 2
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| Contents |
Intro -- Preface -- Contents -- Introduction -- 1 Context and Definition -- 2 Characteristics and Design Goals -- 3 Security and Hardening -- 4 Intelligence -- 5 Summary -- References -- Machine Learning Construction: Implications to Cybersecurity -- 1 Introduction -- 1.1 Motivation -- 1.2 Notation -- 1.3 Roadmap -- 2 Statistical Decision Theory -- 2.1 Regression -- 2.2 Classification -- 2.3 Where Is Learning? -- 3 Parametric Regression and Classification -- 3.1 Linear Models (LM) -- 3.2 Generalized Linear Models (GLM) -- 3.3 Nonlinear Models -- 4 Nonparametric Regression and Classification |
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4.1 Smoothing Techniques -- 4.2 Additive Models (AM) -- 4.3 Generalized Additive Models (GAM) -- 4.4 Projection Pursuit Regression (PPR) -- 4.5 Neural Networks (NN) -- 5 Optimization -- 5.1 Introduction -- 5.2 Connection to Machine Learning -- 5.3 Types of MOP -- 6 Performance -- 6.1 Error Components -- 6.2 Receiver Operating Characteristic (ROC) Curve -- 6.3 The True Performance Is A Random Variable! -- 6.4 Bias-Variance Decomposition -- 6.5 Curse of Dimensionality -- 6.6 Performance of Unsupervised Learning -- 6.7 Classifier Calibration -- 7 Discussion and Conclusion -- References |
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Machine Learning Assessment: Implications to Cybersecurity -- 1 Introduction -- 1.1 Motivation -- 1.2 Notation -- 1.3 Roadmap -- 2 Nonparametric Methods for Estimating the Bias and the Variance of a Statistic -- 2.1 Bootstrap Estimate -- 2.2 Jackknife Estimate -- 2.3 Bootstrap Versus Jackknife -- 2.4 Influence Function, Infinitesimal Jackknife, and Estimate of Variance -- 3 Nonparametric Methods for Estimating the Error Rate of a Classification Rule -- 3.1 Apparent Error -- 3.2 Cross Validation (CV) -- 3.3 Bootstrap Methods for Error Rate Estimation |
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3.4 Estimating the Standard Error of Error Rate Estimators -- 4 Nonparametric Methods for Estimating the AUC of a Classification Rule -- 4.1 Construction of Nonparametric Estimators for AUC -- 4.2 The Leave-Pair-Out Boostrap (LPOB) ModifyingAbove upper A upper U upper C With caret Super Subscript left parenthesis 1 comma 1 right parenthesisAUC""0362AUC( 1,1) , Its Smoothness and Variance Estimation -- 4.3 Estimating the Standard Error of AUC Estimators -- 5 Illustrative Numerical Examples -- 5.1 Error Rate Estimation -- 5.2 AUC Estimation -- 5.3 Components of Variance and Weak Correlation |
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5.4 Two Competing Classifiers -- 6 Discussion and Conclusion -- 7 Appendix -- 7.1 Proofs -- 7.2 More on Influence Function (IF) -- 7.3 ML in Other Fields -- References -- A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms -- 1 Introduction -- 2 Mil-STD-1553 Baseline -- 2.1 Major Components -- 2.2 Bus Communication -- 3 Mil-Std-1553 Attack Vectors -- 3.1 Assumptions and Attacker Position/foothold on 1553 Platform -- 3.2 Attack Vectors and Types -- 4 Simulation and IDS Dataset Generation -- 4.1 Simulation Setup -- 4.2 Baseline Scenarios and Datasets |
| Summary |
This book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to address the challenges faced by this emerging field. Cyber-physical systems bridge the divide between cyber and physical-mechanical systems by combining seamlessly software systems, sensors, and actuators connected over computer networks. Through these sensors, data about the physical world can be captured and used for smart autonomous decision-making. This book introduces fundamental AI/ML principles and concepts applied in developing secure and trustworthy CPS, disseminates recent research and development efforts in this fascinating area, and presents relevant case studies, examples, and datasets. We believe that it is a valuable reference for students, instructors, researchers, industry practitioners, and related government agencies staff |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed December 19, 2022) |
| Subject |
Artificial intelligence.
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Cooperating objects (Computer systems)
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System safety.
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artificial intelligence.
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Artificial intelligence
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Cooperating objects (Computer systems)
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System safety
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| Form |
Electronic book
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| Author |
Traore, Issa, 1965- editor.
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Woungang, Isaac, editor.
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Saad, Sherif, editor
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| ISBN |
9783031162374 |
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3031162374 |
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