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001453360 020__ $$a9783031162374$$q(electronic bk.)
001453360 020__ $$a3031162374$$q(electronic bk.)
001453360 020__ $$z9783031162367
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001453360 0247_ $$a10.1007/978-3-031-16237-4$$2doi
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001453360 08204 $$a006.3$$223/eng/20221219
001453360 24500 $$aArtificial intelligence for cyber-physical systems hardening /$$cIssa Traore, Isaac Woungang, Sherif Saad, editors.
001453360 264_1 $$aCham :$$bSpringer,$$c[2023]
001453360 264_4 $$c©2023
001453360 300__ $$a1 online resource (xiv, 233 pages) :$$billustrations (chiefly color).
001453360 336__ $$atext$$btxt$$2rdacontent
001453360 337__ $$acomputer$$bc$$2rdamedia
001453360 338__ $$aonline resource$$bcr$$2rdacarrier
001453360 4901_ $$aEngineering cyber-physical systems and critical infrastructures ;$$vvolume 2
001453360 5050_ $$aIntro -- Preface -- Contents -- Introduction -- 1 Context and Definition -- 2 Characteristics and Design Goals -- 3 Security and Hardening -- 4 Intelligence -- 5 Summary -- References -- Machine Learning Construction: Implications to Cybersecurity -- 1 Introduction -- 1.1 Motivation -- 1.2 Notation -- 1.3 Roadmap -- 2 Statistical Decision Theory -- 2.1 Regression -- 2.2 Classification -- 2.3 Where Is Learning? -- 3 Parametric Regression and Classification -- 3.1 Linear Models (LM) -- 3.2 Generalized Linear Models (GLM) -- 3.3 Nonlinear Models -- 4 Nonparametric Regression and Classification
001453360 5058_ $$a4.1 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
001453360 5058_ $$aMachine Learning Assessment: Implications to Cybersecurity -- 1 Introduction -- 1.1 Motivation -- 1.2 Notation -- 1.3 Roadmap -- 2 Nonparametric Methods for Estimating the Bias and the Variance of a Statistic -- 2.1 Bootstrap Estimate -- 2.2 Jackknife Estimate -- 2.3 Bootstrap Versus Jackknife -- 2.4 Influence Function, Infinitesimal Jackknife, and Estimate of Variance -- 3 Nonparametric Methods for Estimating the Error Rate of a Classification Rule -- 3.1 Apparent Error -- 3.2 Cross Validation (CV) -- 3.3 Bootstrap Methods for Error Rate Estimation
001453360 5058_ $$a3.4 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
001453360 5058_ $$a5.4 Two Competing Classifiers -- 6 Discussion and Conclusion -- 7 Appendix -- 7.1 Proofs -- 7.2 More on Influence Function (IF) -- 7.3 ML in Other Fields -- References -- A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms -- 1 Introduction -- 2 Mil-STD-1553 Baseline -- 2.1 Major Components -- 2.2 Bus Communication -- 3 Mil-Std-1553 Attack Vectors -- 3.1 Assumptions and Attacker Position/foothold on 1553 Platform -- 3.2 Attack Vectors and Types -- 4 Simulation and IDS Dataset Generation -- 4.1 Simulation Setup -- 4.2 Baseline Scenarios and Datasets
001453360 506__ $$aAccess limited to authorized users.
001453360 520__ $$aThis book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to address the challenges faced by this emerging field. Cyber-physical systems bridge the divide between cyber and physical-mechanical systems by combining seamlessly software systems, sensors, and actuators connected over computer networks. Through these sensors, data about the physical world can be captured and used for smart autonomous decision-making. This book introduces fundamental AI/ML principles and concepts applied in developing secure and trustworthy CPS, disseminates recent research and development efforts in this fascinating area, and presents relevant case studies, examples, and datasets. We believe that it is a valuable reference for students, instructors, researchers, industry practitioners, and related government agencies staff.
001453360 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 19, 2022).
001453360 650_0 $$aArtificial intelligence.
001453360 650_0 $$aCooperating objects (Computer systems)
001453360 650_0 $$aSystem safety.
001453360 655_0 $$aElectronic books.
001453360 7001_ $$aTraore, Issa,$$d1965-$$eeditor.$$1https://isni.org/isni/0000000122529157
001453360 7001_ $$aWoungang, Isaac,$$eeditor.$$1https://isni.org/isni/0000000120291085
001453360 7001_ $$aSaad, Sherif,$$eeditor.
001453360 77608 $$iPrint version:$$z3031162366$$z9783031162367$$w(OCoLC)1338198504
001453360 830_0 $$aEngineering cyber-physical systems and critical infrastructures ;$$vvolume 2.
001453360 852__ $$bebk
001453360 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-16237-4$$zOnline Access$$91397441.1
001453360 909CO $$ooai:library.usi.edu:1453360$$pGLOBAL_SET
001453360 980__ $$aBIB
001453360 980__ $$aEBOOK
001453360 982__ $$aEbook
001453360 983__ $$aOnline
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