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[null,null,"Titolo",null,"Security and resilience in cyber-physical systems : detection, estimation and control / / Masoud Abbaszadeh and Ali Zemouche, editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-030-97166-X"]
[null,null,"Descrizione fisica",null,"1 online resource (383 pages)"]
[null,null,"Disciplina",null,"344.0533"]
[null,null,"Soggetti",null,"Computer security"]
[{"$nulls":4},"Cooperating objects (Computer systems)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Contributors -- 1 Overview -- 2 Introduction to Cyber-Physical Security and Resilience -- 2.1 Introduction -- 2.2 Cyber-Physical Security and Resilience Functionality Overview -- 2.3 Cyber-Physical Security Versus Adjacent Fields -- 2.3.1 Cyber-Physical Security Versus Cyber-Security -- 2.3.2 Cyber-Physical Security Versus FDII -- 2.3.3 Cyber-Physical Security Versus Prognostics -- 2.4 Attack Detection, Isolation, and Identification -- 2.4.1 Model-Based ADII -- 2.4.2 Data-Driven ADII -- 2.5 Attack Resilience -- 2.6 Resilient Estimation -- 2.6.1 State of the Art on Resilient and Secure Estimation: A Glimpse on Existing Methods -- 2.7 Resilient Control -- 2.7.1 Centralized Secure Control -- 2.7.2 Distributed Secure Control -- 2.7.3 Resource-Aware Secure Control -- References -- 3 Fundamental Stealthiness-Distortion Trade-Offs in Cyber-Physical Systems -- 3.1 Introduction -- 3.2 Preliminaries -- 3.3 Stealthiness-Distortion Trade-Offs and Worst-Case Attacks -- 3.3.1 Open-Loop Dynamical Systems -- 3.3.2 Feedback Control Systems -- 3.4 Simulation -- 3.5 Conclusion -- References -- 4 Predictive Situation Awareness and Anomaly Forecasting in Cyber-Physical Systems -- 4.1 Introduction -- 4.2 Forecasting Framework -- 4.2.1 Digital Twin Simulation Platform -- 4.2.2 Anomaly Forecasting Approaches -- 4.2.3 Dimensionality Reduction -- 4.2.4 Forecasting Process -- 4.2.5 Feature Discovery -- 4.3 Ensemble Forecasting -- 4.3.1 Ensemble Modeling in Feature Space -- 4.3.2 Adjusting Cluster Centroids to Physical Points -- 4.3.3 Dynamic Modeling -- 4.3.4 Dynamic Ensemble Forecast Averaging -- 4.3.5 Receding Horizon "]
[{"$nulls":4},"Anomaly Forecast -- 4.3.6 Committed Horizon Anomaly Forecast -- 4.4 Predictive Situation Awareness -- 4.5 Simulation Results -- 4.6 Conclusions -- References."]
[{"$nulls":4},"5 Resilient Observer Design for Cyber-Physical Systems with Data-Driven Measurement Pruning -- 5.1 Notation -- 5.2 Introduction -- 5.3 Concurrent Models -- 5.3.1 Physical Model and Monitor -- 5.3.2 Threat Model -- 5.3.3 Data-Driven Auxiliary Measurement Prior -- 5.3.4 Prior Pruning -- 5.4 Pruning-Based Resilient Estimation -- 5.4.1 Unconstrained ell1 Observer -- 5.4.2 Resilient Pruning Observer -- 5.5 Simulation Results -- 5.5.1 Resilient Power Grid -- 5.5.2 Resilient Water Distribution System -- 5.5.3 Resilient Wheeled Mobile Robot -- 5.6 Conclusion -- References -- 6 Framework for Detecting APTs Based  on Steps Analysis and Correlation -- 6.1 Introduction -- 6.1.1 Targeted APT Attack on CPSs -- 6.1.2 Safety of Cyber-Physical Systems (CPSs) -- 6.1.3 Organization of Book Chapter -- 6.2 Advanced Persistent Threats (APTs) -- 6.2.1 Characteristics of APTs -- 6.2.2 Life Cycle of APTs Attack -- 6.2.3 Related Work -- 6.3 APT Detection Framework -- 6.3.1 Architectural Design of APT-DASAC -- 6.3.2 Three Layers of APT-DASAC -- 6.4 Implementation of APT-DASAC Approach -- 6.4.1 Implementation Setup -- 6.4.2 Implementation Dataset -- 6.5 Experimental Evaluation of APT-DASAC Approach -- 6.5.1 Result and Discussion -- 6.6 Conclusion -- References -- 7 Resilient State Estimation and Attack Mitigation in Cyber-Physical Systems -- 7.1 Introduction -- 7.1.1 Literature Review -- 7.2 Problem Formulation -- 7.2.1 Attack Modeling -- 7.2.2 System Description -- 7.2.3 Security Problem Statement -- 7.3 Resilient State Estimation -- 7.3.1 Multiple-Model State and Input Filtering/Estimation Algorithm -- 7.3.2 Properties of the Resilient State Estimator -- 7.3.3 Fundamental Limitations of Attack-Resilient Estimation -- 7.4 Attack Detection and Identification -- 7.4.1 Attack Detection -- 7.4.2 Attack Identification -- 7.5 Attack Mitigation -- 7.6 Simulation Examples."]
[{"$nulls":4},"7.6.1 Benchmark System (Signal Magnitude , Location Attacks) -- 7.6.2 IEEE 68-Bus Test System (Mode and Signal Magnitude Attacks) -- 7.7 Conclusion -- References -- 8 State and Attacks Estimation  for Nonlinear Takagi-Sugeno Multiple Model Systems with Delayed Measurements -- 8.1 Introduction -- 8.1.1 Contributions and Outline -- 8.1.2 Chapter Organization -- 8.2 Problem Statement -- 8.2.1 False Data Injection Attacks on Actuators/Sensors -- 8.2.2 Polytopic Modeling of Time-Varying Nonlinear Systems with Delayed Measurements -- 8.2.3 Polytopic Modeling of Time-Varying Parameters (Malicious Attacks) -- 8.2.4 LPV Model of Physical Plant Under Data Deception Attacks and Delayed Measurements -- 8.3 Main Result: Observer Design -- 8.4 Numerical Simulation -- 8.4.1 LPV Representation of The Process -- 8.4.2 Date Deception Attacks Representation  on The Actuator/Sensor -- 8.4.3 Simulation Results -- 8.5 Conclusions -- References -- 9 Secure Estimation Under Model Uncertainty -- 9.1 Introduction -- 9.1.1 Overview and Contributions -- 9.1.2 Related Studies -- 9.2 Data Model and Definitions -- 9.2.1 Attack Model -- 9.2.2 Decision Cost Functions -- 9.3 Secure Parameter Estimation -- 9.4 Secure Parameter Estimation: Optimal Decision Rules -- 9.5 Case Studies: Secure Estimation in Sensor Networks -- 9.5.1 Case 1: One Sensor Vulnerable to Causative Attacks -- 9.5.2 Case 2: Both Sensors Vulnerable to Adversarial Attacks -- 9.6 Conclusions -- References -- 10 Resilient Control of Nonlinear Cyber-Physical Systems: Higher-Order Sliding Mode Differentiation and Sparse Recovery-Based Approaches -- 10.1 Introduction -- 10.2 Mathematical Modeling -- 10.2.1 Problem Statement -- 10.3 Preliminary: Sparse Recovering Algorithm -- 10.4 Attack Reconstruction When the Number "]
[{"$nulls":4},"of Potential Attacks is Greater Than the Number of Sensors -- 10.4.1 System Transformation."]
[{"$nulls":4},"10.4.2 Attack Reconstruction -- 10.5 Attack Reconstruction When the Number of Sensors is Greater Than the Number of Potential Sensor Attacks -- 10.5.1 State Attack Reconstruction -- 10.5.2 Sensor Attacks Reconstruction -- 10.6 Case Study: Cyber Attack Reconstruction in the US Western Electricity Coordinating Council Power System -- 10.6.1 Mathematical Model of Electrical Power Network -- 10.6.2 Transformation of DAE to ODE -- 10.6.3 Parameterization of Mathematical Model of Western Electricity Coordinating Council Power System -- 10.6.4 Reconstruction of Attacks via Sparse Recovery Algorithm: The Number of Potential Attacks  is Greater Than the Number of Sensors -- 10.6.5 Reconstruction of Attacks and Estimation of States: The Number of Sensors is Greater Than the Number of Potential Sensor Attacks -- 10.7 Conclusions -- References -- 11 Resilient Cooperative Control of Input Constrained Networked Cyber-Physical Systems -- 11.1 Introduction -- 11.1.1 Notation -- 11.1.2 Preliminaries on Algebraic Graph Theory -- 11.1.3 Preliminaries on Finite-Time Stability -- 11.2 Input Constrained Robust Consensus Tracking for High-Order NCPS -- 11.2.1 Problem Formulation -- 11.2.2 Input Constrained Robust Consensus Tracking with a Static Leader -- 11.2.3 Input Constrained Robust Consensus Tracking with a Dynamic Leader -- 11.2.4 Output-Based Input Constrained Robust Consensus Tracking -- 11.3 Input Constrained Robust Finite-Time Consensus Tracking for High-Order NCPS -- 11.3.1 Problem Formulation -- 11.3.2 Input Constrained Robust Finite-Time Consensus Tracking with Relative State Measurements -- 11.3.3 Input Constrained Robust Finite-Time Consensus Tracking with Relative Output Measurements -- 11.4 Numerical Examples -- 11.4.1 Input Constrained Robust Consensus Tracking for High-Order NCPS."]
[{"$nulls":4},"11.4.2 Input Constrained Robust Finite-Time Consensus Tracking for High-Order NCPS -- 11.5 Conclusions -- References -- 12 Optimal Subsystem Decomposition and Resilient Distributed State Estimation for Wastewater Treatment Plants -- 12.1 Introduction -- 12.2 Model Description of Wastewater Treatment Plants -- 12.3 Subsystem Decomposition -- 12.4 Resilient Distributed State Estimator Design -- 12.5 Simulation -- 12.5.1 Subsystem Decomposition -- 12.5.2 Resilient Distributed State Estimator Design -- 12.6 Conclusion -- References -- 13 Cyber-Attack Detection for a Crude Oil Distillation Column -- 13.1 Introduction -- 13.1.1 Preliminary -- 13.1.2 Cyber-Security of Distillation Column -- 13.2 Distillation Column Design and Modeling -- 13.2.1 Plant Data -- 13.2.2 Distillation Column Design -- 13.2.3 Dynamic Model of the Distillation Column -- 13.2.4 Control of Distillation Column -- 13.3 Testbed Design -- 13.4 Attack Modeling -- 13.5 Attack Detection Algorithm -- 13.5.1 UKF Based Attack Detection -- 13.5.2 Detector Design -- 13.6 Results -- 13.6.1 Attack on Distillate Purity Measurement -- 13.6.2 Attack on Bottoms Impurity Measurement -- 13.6.3 Attack on Reflux Flow Rate -- 13.6.4 Attack Case Summary -- 13.7 Conclusion and Future Work -- References -- 14 A Resilient Nonlinear Observer for Light-Emitting Diode Optical Wireless Communication Under Actuator Fault and Noise Jamming -- 14.1 Introduction -- 14.2 LED-Based Optical Channel Modeling -- 14.2.1 System Setup -- 14.2.2 Luminous Flux Model -- 14.2.3 Model Calibration -- 14.2.4 State-Space and Output Measurement Equations -- 14.3 LED System Model Representation Under Actuator Fault and Noise Jamming Attack -- 14.4 Resilient Observer-Based Tracking Control Design -- 14.4.1 Problem Formulation -- 14.4.2 Unknown Input Observer Design Method -- 14.4.3 Feasibility of (14.24) for Non-"]
[{"$nulls":4},"monotonic Outputs."]
[{"$nulls":4},"14.4.4 A Switched-Gain-Based Observer Solution."]