Securing IoT and Big Data: Next Generation Intelligence

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgements
List of Editors
List of Contributors
Chapter 1: Foundation of Big Data and Internet of Things: Applications and Case Study
	1.1 Introduction to Big Data and Internet of Things
		1.1.1 Big Data Management Systems in Healthcare
		1.1.2 Challenges in Healthcare
		1.1.3 Sequencing Genomic Data
		1.1.4 Deep Learning Applied to Genomic Data
		1.1.5 Genomic Data and Modern Healthcare
	1.2 Background and Rise of Internet of Things
		1.2.1 IoT in Real-Time Healthcare Applications
			1.2.1.1 Wearable Front-End Device
			1.2.1.2 Smartphone Application
			1.2.1.3 Cloud and Algorithms
			1.2.1.4 How Does It Work in Real-Time?
	1.3 Summary
	References
Chapter 2: Securing IoT with Blockchain: Challenges, Applications, and Techniques
	2.1 Introduction
	2.2 Security Issues of IoT
		2.2.1 IoT Malware
		2.2.2 Device Updates Management
		2.2.3 Manufacturing Defects
		2.2.4 Security of Massively Generated Data
		2.2.5 Authorization and Authentication Issues
		2.2.6 Botnet Attacks
	2.3 Introduction to Blockchain
		2.3.1 Public Blockchain
		2.3.2 Private Blockchain
		2.3.3 Consortium Blockchain
	2.4 Blockchain and IoT Integration: An Overview
	2.5 Applications of Integration
		2.5.1 Smart Homes and Cities
		2.5.2 Healthcare
		2.5.3 Internet of Vehicles
		2.5.4 Smart Manufacturing
		2.5.5 Supply Chain
		2.5.6 Smart Energy Grids
	2.6 Existing Research on Blockchain-Based IoT Security
		2.6.1 Lightweight IoT Nodes as Thin Clients
		2.6.2 IoT Gateways as Blockchain Nodes
		2.6.3 IoT Nodes Integrated with Blockchain Clients
		2.6.4 IoT Nodes as Regular Sensors
	2.7 Conclusion and Future Work
	References
Chapter 3: IoT and Big Data Using Intelligence
	3.1 IoT in a Nutshell
	3.2 The Buzzword: Big Data in a Nutshell
	3.3 IoT vs Big Data
	3.4 Data Generation – Machine vs Human
		3.4.1 Machine-Generated Data
		3.4.2 Human-Generated Data
	3.5 Data Stream, Management, and Progression Using IoT and Big Data Approach
		3.5.1 Data Streaming in IoT
	3.6 IoT and Big Data Working Together Using Intelligence
		Stage 1: Device Connectivity and Data Forwarding
		Stage 2: Real-Time Monitoring
		Stage 3: Automated Analytics – Big Data Analytics and Complex Event Processing
			Machine Learning Algorithm
		Stage 4: Automation
		Stage 5: On-Board Intelligence
	3.7 Working Challenges
		3.7.1 Component Convergence Challenges (CCC)
		3.7.2 Embedded Network Challenges (ENC)
		3.7.3 Analytics and Application Challenges (AAC)
		3.7.4 Ethical and Security Challenges (ESC)
		3.7.5 IoT Adoption Challenges (IAC)
	3.8 Conclusion
	References
Chapter 4: Compulsion for Cyber Intelligence for Rail Analytics in IoRNT
	4.1 Introduction
	4.2 Computer-Based Intelligence
		4.2.1 Cyber Threat Intelligence
		4.2.2 Big Data and Analytics
	4.3 Analytics Types: Descriptive, Prescriptive, and Predictive
	4.4 Understanding Predictive and Concise Analysis
		4.4.1 Analytical Methods
		4.4.2 Descriptive Analytics
		4.4.3 Predictive Analytics
		4.4.4 Prescriptive Analytics
	4.5 Railway Networks
		4.5.1 Industry Pan Rail Directions
			4.5.1.1 Marketplace Size
			4.5.1.2 Investment/Evolution
			4.5.1.3 Government Initiatives
			4.5.1.4 Road Ahead
	4.6 Rail Analytics
	4.7 Internet of Rail Network Things
		4.7.1 From Application Enabling Interface to IoT
		4.7.2 Investing in Intelligence
	4.8 Big Data in Rail Intelligence Based on Cyber Threat
		4.8.1 An Effective Cyber Security Strategy for the Rail Sector
			4.8.1.1 Dedicated Skills
				4.8.1.1.1 Sectorial and Cross-sectorial Cooperation
				4.8.1.1.2 Security-by-Design
				4.8.1.1.3 Research and Innovation (R&I)
				4.8.1.1.4 Working Together with the EU Institutions
	4.9 Cyber Security Risk Management Strategic and Tactical Capabilities
	4.10 Cyber-Attacks Affecting Railways
	4.11 Railway Cyber Security: Railway Operations and Assets Security
		4.11.1 Cyber Security Railway Future Vulnerabilities
		4.11.2 Cyber Security in the Fight Against Railways
	4.12 Conclusion
	References
Chapter 5: Big Data and IoT Forensics
	5.1 Background and Introduction
	5.2 Types of IoT Forensics
		5.2.1 Cloud Forensics
		5.2.2 Network Forensics
		5.2.3 Device-Level Forensics
	5.3 Sources and Nature of Data
		5.3.1 Big Data
		5.3.2 IoT Forensics Data
	5.4 Role of Big Data in IoT Forensics
		5.4.1 Big Data Technologies
			5.4.1.1 Hadoop
			5.4.1.2 Spark
			5.4.1.3 Kafka
		5.4.2 Big Data Analytics
			5.4.2.1 Data Stream Learning
			5.4.2.2 Deep Learning
			5.4.2.3 Incremental and Ensemble Learning
			5.4.2.4 Granular Computing-Based Machine Learning
	5.5 IoT Forensics Investigation Framework
		5.5.1 Steps for IoT Forensics Investigation
			5.5.1.1 Evidence Collection
			5.5.1.2 Examination
			5.5.1.3 Analysis
			5.5.1.4 Reporting
		5.5.2 Forensic Soundness
			5.5.2.1 Meaning
			5.5.2.2 Errors
			5.5.2.3 Transparency and Trustworthiness
			5.5.2.4 Experience
	5.6 Challenges in IoT Forensics
		5.6.1 Variety of Data
		5.6.2 Security
		5.6.3 Privacy
		5.6.4 Data Organization
	5.7 Case Studies Using IoT Forensics
		5.7.1 Smart Health Monitoring System
		5.7.2 Amazon Echo as a Use Case
		5.7.3 IoT in a Smart Home
	5.8 Solution Methodology Proposed
		5.8.1 Machine Learning Algorithms
		5.8.2 Public Digital Ledger
	5.9 Opportunities and Future Technologies
		5.9.1 Forensic Data Dependability
		5.9.2 Models and Tools
		5.9.3 Smart Analysis and Presentation
		5.9.4 Resolving Legal Challenges
		5.9.5 Smart Forensics for IoT
		5.9.6 Emerging Technologies for IoT
	5.10 Conclusion
	References
Chapter 6: Integration of IoT and Big Data in the Field of Entertainment for Recommendation System
	6.1 Introduction
	6.2 Background
	6.3 Analysis and Algorithms
	6.4 Case Study
	6.5 Discussion
	6.6 Conclusion
	References
Chapter 7: Secure and Privacy Preserving Data Mining and Aggregation in IoT Applications
	7.1 Introduction
	7.2 Privacy and Security Challenges in IoT Applications
		7.2.1 Identification
		7.2.2 Localizing and Tracking
		7.2.3 Life Cycle Transitions
		7.2.4 Secure Data Transmission
	7.3 Secure and Privacy Preserving Data Mining Techniques
		7.3.1 Privacy Preserving Techniques at Data Collection Layer
			7.3.1.1 Additive Noise
			7.3.1.2 Multiplicative Noise
		7.3.2 Privacy Preserving at Data Publishing Layer
			7.3.2.1 Generalization
			7.3.2.2 Suppression
			7.3.2.3 Anatomization
			7.3.2.4 K-Anonymity
			7.3.2.5 L-Diversity
			7.3.2.6 Personalized Privacy
			7.3.2.7 Differential Privacy
			7.3.2.8 ∈-Differential Privacy
		7.3.3 Privacy Preserving at Data Mining Output Layer
			7.3.3.1 Association Rule Hiding
			7.3.3.2 Classifier Effectiveness Downgrading
			7.3.3.3 Query Auditing and Inference Control
		7.3.4 Distributed Privacy
			7.3.4.1 One out of Two Oblivious Transfer
			7.3.4.2 Homomorphic Encryption
	7.4 Security Ensuring Techniques for Privacy Preserving Data Aggregation
		7.4.1 Privacy Preservation Using Homomorphic Encryption and Advanced Encryption Standard (AES)
			7.4.1.1 Implementation of Homomorphic Encryption and AES Algorithm
			7.4.1.2 Encryption and Exchange
			7.4.1.3 Decryption and Confusion
			7.4.1.4 Encryption and Reporting
			7.4.1.5 Verification and Aggregation
			7.4.1.6 Security Analysis
			7.4.1.7 Performance Evaluation
		7.4.2 Evolutionary Game-Based Secure Private Data Aggregation
		7.4.3 Slice-Mix-Aggregate and iPDA
	7.5 Conclusion
	List of Abbreviations
	References
Chapter 8: Real-Time Cardiovascular Health Monitoring System Using IoT and Machine Learning Algorithms: Survey
	8.1 Introduction
	8.2 Cardiovascular Diseases (CVD)
	8.3 Motivation and Classification
		8.3.1 Internet of Things (IoT)
		8.3.2 IoT Applications
		8.3.3 IoT in Healthcare
		8.3.4 Machine Learning
		8.3.5 Machine Learning in Healthcare
	8.4 Comparison of Healthcare Monitoring Systems Under IoT
	8.5 Machine Learning Algorithms Implemented in CVD Healthcare Monitoring System
		8.5.1 Implementation of Random Forest and KNN for CVD Health Data
		8.5.2 Implementation Results
	8.6 Role of Fog and Edge Computing
	8.7 Issues and Challenges
		8.7.1 General Issues in Machine Learning Algorithms
		8.7.2 Issues and Challenges of IoT in Healthcare
		8.7.3 Issues and Challenges in Fog and Edge Computing
	8.8 Conclusion
	References
Index
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