Artificial Intelligence for Cyber-Physical Systems Hardening

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
		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
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
		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
		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
		4.3 Attack Scenarios and Datasets
	5 Conclusion
	References
Unsupervised Anomaly Detection for MIL-STD-1553 Avionic Platforms Using CUSUM
	1 Introduction
	2 Datasets
	3 Features Model
	4 Detection Model
		4.1 Change Point Detection
		4.2 Using CUSUM Algorithm
	5 Empirical Evaluation
		5.1 Performance Metrics
		5.2 Evaluation Procedures
		5.3 Evaluation Results
		5.4 Results Discussion
	6 Conclusion
	References
Secure Design of Cyber-Physical Systems at the Radio Frequency Level: Machine and Deep Learning-Driven Approaches, Challenges and Opportunities
	1 Introduction to Cyber-Physical Systems
		1.1 Security and Application Areas of CPS
	2 Critical Infrastructures
	3 Fundamentals of Radio Frequency Fingerprinting
	4 Application Areas of RF Security
		4.1 Authentication
		4.2 Geo-location and Tracking
		4.3 Intrusion Detection
		4.4 Interference Detection and Traditional Approaches
		4.5 Anti-spoofing Solutions
	5 Machine and Deep Learning-Based RF Security for Cyber-Physical Systems
		5.1 Machine Learning Based Approaches for RF Fingerprinting
		5.2 Proactive, Adaptive Machine Learning Based Approaches for RF Interference Detection
		5.3 Machine Learning Based Anti-spoofing Approaches for RF Security
		5.4 Machine Learning Based Antijamming Approaches for RF Security
	6 Open Issues, Challenges and Opportunities in Secure Cyber-Physical Systems via RF Fingerprinting
		6.1 Impact of Receiver Hardware
		6.2 Robustness in Realistic Operation Environments
		6.3 Simulation-Reality Gap
		6.4 Finding a Realistic Dataset
		6.5 Feature Selection
		6.6 Other Open Issues
	7 Summary
	References
Attack Detection by Using Deep Learning for Cyber-Physical System
	1 Introduction
	2 CPSs and DLs
	3 Different DL Models in CPSs
		3.1 Convolutional Neural Networks (CNNs)
		3.2 Auto Encoder (AE)
		3.3 Deep Belief Network (DBN)
		3.4 Recurrent Neural Network (RNN)
	4 Leveraging DL to Detect Attacks in CPSs
		4.1 Using Convolutional Neural Networks (CNNs)
		4.2 Using Auto Encoder (AE)
		4.3 Using Deep Belief Network (DBN)
		4.4 Using Recurrent Neural Network (RNN)
	5 Leveraging RL and DL in Detecting Cyberattacks in CPS
		5.1 Using Reinforcement Learning (RL)
		5.2 Deep Reinforcement Learning (DRL)
	6 Data Acquisition in CPSs
	7 Challenges to Attack Detection in CPSs
	8 Robust Attacks Detection
	9 Conclusion
	References
Security and Privacy of IoT Devices  for Aging in Place
	1 Introduction
	2 Review of IOT Devices for Aging in Place
		2.1 Device Types
		2.2 Use Cases
	3 AIP Threats and Vulnerabilities
		3.1 Threats Speficic to AgeTech Environment
		3.2 Common Threats
		3.3 Device Specific Threats
		3.4 Mitigation Strategies
	4 Using Machine Learning and AI Models
		4.1 Available Datasets
		4.2 Existing ML-Based Proposals
	5 Conclusion
	References
Detecting Malicious Attacks Using Principal Component Analysis in Medical Cyber-Physical Systems
	1 Introduction
	2 Concepts of Intrusion Detection Systems
	3 PCA Based Anomaly Detection
	4 Experimental Evaluations
	5 Conclusions and Future Work
	References
Activity and Event Network Graph and Application to Cyber-Physical Security
	1 Introduction
	2 AEN Graph Theoretic Model
	3 AEN Data Sources
	4 Graph Model Elements
		4.1 AEN Nodes
		4.2 AEN Edges
	5 AEN Probability Model
		5.1 Probability Model Definition
		5.2 Probability Model Usage and Application
	6 Graph Construction and Framework Implementation
		6.1 Framework Architecture
		6.2 Case Study Based on a Cyperphysical Security Dataset
	7 Conclusion
	References