AI-Enabled Threat Detection and Security Analysis for Industrial IoT

Preface
Contents
Artificial Intelligence for Threat Detection and Analysis in Industrial IoT: Applications and Challenges
	1 Introduction
	2 Book Outline
	References
Complementing IIoT Services Through AI: Feasibility and Suitability
	1 Introduction
	2 IIoT with Edge Intelligence
		2.1 The Challenges of EC in IIoT
		2.2 Classifications of AI Techniques
		2.3 Machine Learning Techniques in IIoT
		2.4 Machine Learning Techniques in Edge Computing
		2.5 Edge Intelligent IIoT
	3 AI-Enhanced Cooperative Computing Architecture
	4 Potential Advantages of Learning Techniques in Edge Intelligent IIoT
	5 Practical Limitation and Open Issues
		5.1 Software Platforms and Middleware
		5.2 Task Offloading and Load Balancing
		5.3 EI Model Design
		5.4 Security Issues
	6 Conclusion
	References
Data Security and Privacy in  Industrial IoT
	1 Introduction
	2 Intrusion Detection in IIoT
	3 Authentication Techniques
	4 Key Establishment Techniques
		4.1 Key Establishment Protocols at Higher Layers
			4.1.1 Symmetric Key Establishment Protocols
			4.1.2 Asymmetric Key Establishment Protocols
		4.2 Key Establishment Protocols at the Physical Layer
			4.2.1 Key Establishment Protocols Using channel’s Characteristics
			4.2.2 Using Keyless Cryptography
		4.3 Cross-Layer Key Establishment Protocols
			4.3.1 Cross-Layer Key Establishment Protocols Based on Asymmetric Key Setting
			4.3.2 Cross-Layer Key Establishment Protocols Based on Symmetric Key Setting
	5 Real IIoT Security Testbeds
	6 Conclusion
	References
Blockchain Applications in the Industrial Internet of Things
	1 Introduction
	2 Industrial Internet of Things
		2.1 IIoT Architecture
		2.2 IIoT Challenges
	3 Blockchain
		3.1 Blockchain Structure
		3.2 Blockchain Usage in IIoT
	4 Blockchain Applications in IIoT
		4.1 Smart City
		4.2 Manufacturing
		4.3 Healthcare 4.0
		4.4 Energy Management
		4.5 Agriculture 4.0
		4.6 Smart Homes
		4.7 Autonomous Vehicles
		4.8 Multimedia Right Management
	5 Analysis
	6 Challenges and Open Issues
	7 Conclusion
	References
Application of Deep Learning on IoT-Enabled Smart Grid Monitoring
	1 Introduction
	2 Smart Grid State Estimation
	3 Fundamental Concepts for State Estimation Concepts in Active Distribution System
	4 State Estimation Problem in Active Distribution Systems
	5 Various State Estimation Methods Used in Smart Grid
		5.1 Conventional Approach
		5.2 Kalman Filter-Based Approaches
	6 Learning Based Applications in SGSE
		6.1 Support Vector Machine Approaches
		6.2 Bayesian Theorem Approaches
		6.3 Regression Analysis Approaches
		6.4 Artificial Neural Network Approaches
	7 Simulation
		7.1 Case Study 1
		7.2 Case Study 2
	8 Discussion
	References
Cyber Security of Smart Manufacturing Execution Systems: A Bibliometric Analysis
	1 Introduction
	2 Methodology
	3 Findings
		3.1 Productivity
		3.2 Research Areas
		3.3 Institutions
		3.4 Authors
		3.5 Publishers
		3.6 Highly Cited Articles
		3.7 Keywords Frequency
	4 Conclusions
	References
The Role of Machine Learning in IIoT Through FPGAs
	1 Introduction
		1.1 Industrial Internet of Things (IIoT)
		1.2 Challenges of IIot
			1.2.1 Security
			1.2.2 Connectivity
			1.2.3 IIoT Integration
			1.2.4 Data Storage
			1.2.5 Analytics Challenges
	2 Machine Learning
	3 FPGAs
	4 Case Study
	5 Challenges and Open Issues
	6 Conclusion
	References
Deep Representation Learning for Cyber-Attack Detection in Industrial IoT
	1 Introduction
	2 Cyber-Attack Detection
	3 Machine Learning (ML)
		3.1 Deep Neural Network (DNN)
			3.1.1 Autoencoder
			3.1.2 Long Short-Term Memory (LSTM)
		3.2 Decision Tree (DT)
		3.3 K-Nearest Neighbors (KNN)
		3.4 Random Forest (RF)
		3.5 Support Vector Machine (SVM)
		3.6 Naïve Bayes (NB)
		3.7 Challenges of Applying ML on IIoT Data
	4 The Proposed ML-Based Detection Method
		4.1 Data Engineering
		4.2 Data Splitting
		4.3 Training the Proposed Method
	5 Experimental Setup and Evaluation Results
		5.1 Dataset
		5.2 Attack Scenario
		5.3 Evaluation Metrics
		5.4 Evaluation Results
	6 Conclusion
	References
Classification and Intelligent Mining of Anomalies in Industrial IoT
	1 Introduction
	2 Anomaly Detection and Its Challenges in IIoT
	3 Literature Review for Anomaly Detection in IIoT
	4 Discussion
	5 Open Challenges and Future Research Directions
		5.1 Lack of Training Data Sets
		5.2 Real-Time Anomaly Detection
		5.3 Adaptive Learning
		5.4 Resource and Energy Constraints
		5.5 Privacy and Security Concerns
	6 Conclusion
	References
A Snapshot Ensemble Deep Neural Network Model for Attack Detection in Industrial Internet of Things
	1 Introduction
	2 Previous Works in IIoT Security
	3 Methodology
		3.1 Dataset
		3.2 Preprocessing of Data
			3.2.1 Features
			3.2.2 Replacing Missing/NaN Values
		3.3 Snapshot Ensemble Deep Neural Network
		3.4 Evaluation Parameters
	4 Implementation and Results
		4.1 Software and Hardware
		4.2 Results
	5 Conclusion and Future Work
	References
Privacy Preserving Federated Learning Solution for Security of Industrial Cyber Physical Systems
	1 Introduction
	2 Cyber-Physical System (CPS) Security
		2.1 Major Attacks on Cyber-Physical Systems (CPS)
		2.2 Privacy
	3 Federated Learning (FL)
		3.1 Architectures of Federated Learning (FL)
		3.2 Algorithms of FL
		3.3 Challenges and Vulnerabilities of FL
		3.4 Countermeasures
	References
A Multi-Stage Machine Learning Model for Security Analysis in Industrial Control System
	1 Introduction
	2 Background
		2.1 Gas Pipeline System
		2.2 Water Tank Storage System
		2.3 Machine Learning Algorithm
			2.3.1 Decision Tree
			2.3.2 Random Forest
			2.3.3 K-Nearest Neighbors
			2.3.4 Logistic Regression
			2.3.5 Multi-Layer Perceptron Algorithm
	3 Literature Review
		3.1 Types of Cyber Attacks
		3.2 Detection of Cyber Attacks
		3.3 Summary
	4 Proposed Models
		4.1 Dataset Processing
		4.2 Machine Learning Model
		4.3 Summary
	5 Methodology
		5.1 Datasets Collection Methodology
		5.2 Feature Selection Methodology
		5.3 Machine Learning Classifiers
		5.4 Summary
	6 Results and Discussion
		6.1 Model Performance
		6.2 Summary
	7 Conclusions
	References
A Recurrent Attention Model for Cyber Attack Classification
	1 Introduction
	2 Previous Work
	3 Proposed Approach
		3.1 Data Processing and Visualization
		3.2 Recurrent Neural Network (RNN)
		3.3 Reinforcement Learning (RL)
		3.4 Recurrent Attention Model (RAM)
	4 Experimental Analysis
	5 Results
		5.1 IoT Dataset
		5.2 BATADAL Dataset
	6 Discussion
	References