Secure edge computing applications, techniques and challenges

The internet is making our daily life as digital as possible and this new era is called the Internet of Everything (IoE). Edge computing is an emerging data analytics concept that addresses the challenges associated with IoE. More specifically, edge computing facilitates data analysis at the edge of...

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Detalles Bibliográficos
Otros Autores:	Ahmed, Mohiuddin (Computer scientist), editor (editor), Dowland, Paul, editor
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	[S.l.] : CRC Press 2021.
Edición:	1st ed
Materias:	Edge computing. Computer security > Standards.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009633579606719

Tabla de Contenidos:

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgments
Editors
Contributors
Section I
Chapter 1: Secure Fog-Cloud of Things: Architectures, Opportunities and Challenges
1.1 Introduction
1.1.1 Chapter Road Map
1.2 Secure Fog-Cloud of Things
1.2.1 Environment
1.2.2 Architecture
1.3 Threats, Vulnerabilities and Exploits in Fog-Cloud of Things Ecosystems
1.4 Key Machine Learning Kits for Secure Fog-Cloud of Things Architecture
1.5 Applications
1.6 Opportunities and Challenges in Improving Security in Fog-Cloud of Things
1.6.1 Opportunities
1.6.2 Challenges
1.7 Future Trends
1.8 Conclusion
References
Chapter 2: Collaborative and Integrated Edge Security Architecture
2.1 Background
2.2 Edge Security Challenges
2.3 Perspectives of Edge Security Architecture
2.4 Emerging Trends and Enablers for Edge Security Architecture
2.4.1 The Edge Computing Architecture
2.4.2 Leveraging Fog-Based Security Architecture for Edge Networks
2.5 Collaborative and Integrated Security Architecture for Edge Computing
2.5.1 Overview
2.5.2 Distributed Virtual Firewall (DFWs)
2.5.3 Distributed Intrusion Detection Systems (IDSs)
2.6 Conclusion and Future Research
References
Chapter 3: A Systemic IoT-Fog-Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing
3.1 Introduction
3.2 Fog Computing Systems
3.2.1 Description of Fog
3.2.2 Characteristics of Fog
3.2.3 Systemic Architecture of IoT-Fog-Cloud
3.2.4 Applications of IoT, Fog and Cloud Systems
3.3 Cyber Security Challenges
3.4 Security Solutions and Future Directions
3.5 Conclusion
References
Chapter 4: Security and Organizational Strategy: A Cloud and Edge Computing Perspective
4.1 Introduction.
4.2 Cloud Computing and Cloud-based Computing
4.3 Business Operations and Management
4.3.1 Business Process
4.3.2 Business Continuity
4.3.3 Risk Management and Disaster Recovery
4.4 Human and Technological Factors
4.4.1 Human Factors
4.4.2 Technological Factors
4.4.3 Copyright and SLAs
4.5 Trust
4.5.1 Intra-organizational Trust
4.5.2 Inter-organizational Trust
4.6 Geographic Location
4.6.1 Regulations and Jurisdictions
4.6.2 Compliance and Governance
4.7 Conclusions
References
Chapter 5: An Overview of Cognitive Internet of Things: Cloud and Fog Computing
5.1 Introduction
5.2 Background of Fog, Cloud and Edge Computing
5.2.1 Fog Computing
5.2.1.1 Benefits of Fog Computing
5.2.1.2 Disadvantages of Fog Computing
5.2.2 Cloud Computing
5.2.2.1 Benefits of Cloud Computing
5.2.2.2 Disadvantages of Cloud Computing
5.2.3 Edge Computing
5.2.3.1 Benefits of Edge Computing
5.2.3.2 Disadvantages of Edge Computing
5.3 Literature Review of Existing Works
5.3.1 Review of Fog Computing
5.3.2 Review of Cloud Computing
5.3.3 Review of Edge Computing
5.4 Network Architecture
5.4.1 Computation Between Fog and Cloud
5.4.2 Computation Between Fog and Fog
5.5 Numerical Results
5.6 Conclusion
References
Chapter 6: Privacy of Edge Computing and IoT
6.1 Introduction
6.2 IoT Ecosystem
6.3 Privacy Spaces
6.4 The Technology of Privacy Spaces
6.4.1 Apple HomeKit
6.4.2 Google Home
6.5 Privacy Space Data Flows
6.6 Remote Access
6.7 Personal Data Store
6.8 Privacy-Preserving Techniques
6.8.1 Anonymization
6.8.2 k-Anonymization
6.8.3 Unicity
6.8.4 Differential Privacy
6.8.5 Privacy-Preserving Data Queries
6.9 Case Study: Contact Tracking Mobile Applications
6.10 Conclusions
Notes
References
Section II.
Chapter 7: Reducing the Attack Surface of Edge Computing IoT Networks via Hybrid Routing Using Dedicated Nodes
7.1 Introduction
7.2 Related Works
7.3 The Solution
7.3.1 Inference System of Trusted Time Server
7.3.2 Security Features
7.3.3 Synchronization with a Trusted Time Server
7.3.4 Transit Addresses
7.4 Test Methodology and Environment
7.4.1 TTS Server and Data Collection for Inference
7.4.2 Heterogeneous Network Environment
Simulation Case 1:
Simulation Case 2:
7.4.3 Graph-based Representation
7.5 Case Study
7.6 Conclusion
Notes
References
Chapter 8: Early Identification of Mental Health Disorder Employing Machine Learning-based Secure Edge Analytics: A Real-time Monitoring System
8.1 Introduction
8.2 Traditional Methods Implemented in Edge Computing
8.3 Secure Analytics of Smart Healthcare at the Edge
8.4 Related Work: Overview of Mobile Applications for Mental Health
8.4.1 Anxiety Reliever
8.4.2 Anxiety Coach
8.4.3 Breath2Relax
8.4.4 Happify
8.4.5 Head Space
8.4.6 Mindshift
8.4.7 MoodKit
8.4.8 Panic Relief
8.4.9 PTSD Coach
8.5 Methodologies for Automated Real-Time Mood Detection for Assessing Anxiety and Depression Levels in the Edge with Privacy-Preservation Capability
8.5.1 Data Preparation and Pre-processing
Face-tracking
8.5.1.1 Identifying Optic Flow in Facial Regions
8.5.2 Pre-processing and Noise Elimination of the Image Data
8.5.3 Questionnaire Data Description
8.5.4 Proposed Architecture
8.5.5 Data Analysis Using AI Techniques
8.5.6 Privacy Preservation of the Model
8.5.6.1 Federated Learning
8.5.7 Model Deployment on Edge Devices
8.6 Experimental Results
8.6.1 SqlLite Analysis
8.6.2 Machine Learning Algorithm Analysis
8.6.3 Federated Learning Analysis
8.6.4 Comparative Analysis.
8.7 Conclusion
References
Chapter 9: Harnessing Artificial Intelligence for Secure ECG Analytics at the Edge for Cardiac Arrhythmia Classification
9.1 Introduction
9.2 Literature Review
9.3 Dataset Preparation
9.4 Methodology
9.4.1 ECG Pre-processing Phase
9.4.2 Heartbeat Segmentation Phase
9.4.3 Feature Extraction Phase
9.4.4 Learning/Classification Phase
9.5 Experimental Setups, Results and Discussion
9.5.1 Performance Indicators
9.5.2 Results for Experimental Setup 1
9.5.3 Results for Experimental Setup 2
9.6 Conclusion
References
Chapter 10: On Securing Electronic Healthcare Records Using Hyperledger Fabric Across the Network Edge
10.1 Introduction
10.2 Existing Decentralized Security Methods: Can Blockchain Be Used At the Edge?
10.2.1 Current EHR System in Canada
10.2.2 Challenges with the Traditional EHR Systems
10.2.3 Security Measures for Health Records
10.3 Current Challenges Faced by the Healthcare Workers in Covid-19 Pandemic
10.3.1 Importance and Role of Medical Records During Pandemic
10.3.2 Challenges Faced by Doctors
10.3.3 Understanding the Proposed Architecture Using COVID-19 Example
10.4 Scalable Secure Management and Access Control of Electronic Health Records at the Edge
10.4.1 The Importance of Integrating Blockchain and Edge Computing?
10.4.2 Challenges
10.5 Overview of Blockchain and Hyper Ledger Methodologies
10.5.1 Blockchain
10.5.2 Electronic Health Records (EHRs)
10.5.3 Smart Contract
10.5.4 Access Control in Medical Domain
10.5.5 Hyperledger
10.5.6 Composer Tools
10.5.7 Playground
10.5.8 Off-chain Storage
10.5.9 User Experience From Patient's Side
10.6 Hyper Ledger-Based Proposed Architecture for Protecting Electronic Health Records
10.6.1 Proposed Architecture of the Blockchain System.
10.6.2 Data Flow Diagrams
10.6.2.1 Doctors
10.6.2.2 Patient
10.6.2.3 Transaction Flow
10.7 Performance Evaluation
10.7.1 Performance of the Proposed Model
10.7.2 Performance Comparison
10.8 Conclusions and Future Caveats
References
Chapter 11: AI-Aided Secured ECG Live Edge Monitoring System with a Practical Use-Case
11.1 Introduction
11.1.1 Background
11.1.2 Problem Statement
11.1.3 Objective and Scope
11.2 Related Work
11.3 Proposed AI-Based System Architecture
11.3.1 Block Diagram
11.3.2 Data Collection and Pre-Processing Steps
11.3.3 Detecting Heart Abnormalities Using AI-Aided Techniques
11.4 Considered Smart ECG Monitoring System
11.4.1 Edge Hardware Components
11.4.1.1 System-on-a-Chip (SoC) Model
11.4.1.2 IoT Sensor for Heart Rate Data Acquisition
11.4.1.3 Microprocessor and Analog to Digital Converter
11.4.2 AI-Logic Component
11.4.2.1 Decision Tree
11.4.2.2 Random Forest
11.4.2.3 ANN
11.4.2.4 CNN
11.5 Bio-Authentication Application of the Considered ECG Monitoring System for Specific Use-Cases
11.6 Performance Evaluation
11.6.1 Supraventricular Arrhythmia Classification
11.6.2 Authorized User Classification for Bio-Authentication System
11.7 Challenges Involved with the Proposed System
Limitations
11.8 Conclusion and Future Scope
References
Section III
Chapter 12: Application of Unmanned Aerial Vehicles in Wireless Networks: Mobile Edge Computing and Caching
12.1 Introduction
12.1.1 Chapter Roadmap
12.2 Literature Review
12.3 Description of Caching and Mobile Edge Computing
12.3.1 Overview of Caching
12.3.1.1 Advantages
12.3.1.2 Disadvantages
12.3.2 Overview of Mobile Edge Computing
12.3.2.1 Advantages
12.3.2.2 Disadvantages
12.4 Layering of UAV-Based MEC Architecture.
12.4.1 Explanation of the Layers.

Secure edge computing applications, techniques and challenges

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