book

Secure Edge Computing

Name: Secure Edge Computing
ISBN: 9781000427325

by Mohiuddin Ahmed, Paul Haskell-Dowland

August 2021

Intermediate to advanced

304 pages

11h 24m

English

CRC Press

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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 Introduction1.1.1 Chapter Road Map1.2 Secure Fog-Cloud of Things1.2.1 Environment1.2.2 Architecture1.3 Threats, Vulnerabilities and Exploits in Fog-Cloud of Things Ecosystems1.4 Key Machine Learning Kits for Secure Fog-Cloud of Things Architecture1.5 Applications1.6 Opportunities and Challenges in Improving Security in Fog-Cloud of Things1.6.1 Opportunities1.6.2 Challenges1.7 Future Trends1.8 ConclusionReferences
Chapter 2: Collaborative and Integrated Edge Security Architecture
2.1 Background2.2 Edge Security Challenges2.3 Perspectives of Edge Security Architecture2.4 Emerging Trends and Enablers for Edge Security Architecture2.4.1 The Edge Computing Architecture2.4.2 Leveraging Fog-Based Security Architecture for Edge Networks2.5 Collaborative and Integrated Security Architecture for Edge Computing2.5.1 Overview2.5.2 Distributed Virtual Firewall (DFWs)2.5.3 Distributed Intrusion Detection Systems (IDSs)2.6 Conclusion and Future ResearchReferences
Chapter 3: A Systemic IoT–Fog–Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing
3.1 Introduction3.2 Fog Computing Systems3.2.1 Description of Fog3.2.2 Characteristics of Fog3.2.3 Systemic Architecture of IoT–Fog–Cloud3.2.4 Applications of IoT, Fog and Cloud Systems3.3 Cyber Security Challenges3.4 Security Solutions and Future Directions3.5 ConclusionReferences
Chapter 4: Security and Organizational Strategy: A Cloud and Edge Computing Perspective
4.1 Introduction4.2 Cloud Computing and Cloud-based Computing4.3 Business Operations and Management4.3.1 Business Process4.3.2 Business Continuity4.3.3 Risk Management and Disaster Recovery4.4 Human and Technological Factors4.4.1 Human Factors4.4.2 Technological Factors4.4.3 Copyright and SLAs4.5 Trust4.5.1 Intra-organizational Trust4.5.2 Inter-organizational Trust4.6 Geographic Location4.6.1 Regulations and Jurisdictions4.6.2 Compliance and Governance4.7 ConclusionsReferences
Chapter 5: An Overview of Cognitive Internet of Things: Cloud and Fog Computing
5.1 Introduction5.2 Background of Fog, Cloud and Edge Computing5.2.1 Fog Computing5.2.1.1 Benefits of Fog Computing5.2.1.2 Disadvantages of Fog Computing5.2.2 Cloud Computing5.2.2.1 Benefits of Cloud Computing5.2.2.2 Disadvantages of Cloud Computing5.2.3 Edge Computing5.2.3.1 Benefits of Edge Computing5.2.3.2 Disadvantages of Edge Computing5.3 Literature Review of Existing Works5.3.1 Review of Fog Computing5.3.2 Review of Cloud Computing5.3.3 Review of Edge Computing5.4 Network Architecture5.4.1 Computation Between Fog and Cloud5.4.2 Computation Between Fog and Fog5.5 Numerical Results5.6 ConclusionReferences
Chapter 6: Privacy of Edge Computing and IoT
6.1 Introduction6.2 IoT Ecosystem6.3 Privacy Spaces6.4 The Technology of Privacy Spaces6.4.1 Apple HomeKit6.4.2 Google Home6.5 Privacy Space Data Flows6.6 Remote Access6.7 Personal Data Store6.8 Privacy-Preserving Techniques6.8.1 Anonymization6.8.2 k-anonymization6.8.3 Unicity6.8.4 Differential Privacy6.8.5 Privacy-Preserving Data Queries6.9 Case Study: Contact Tracking Mobile Applications6.10 ConclusionsNotesReferences
Section II
Chapter 7: Reducing the Attack Surface of Edge Computing IoT Networks via Hybrid Routing Using Dedicated Nodes
7.1 Introduction7.2 Related Works7.3 The Solution7.3.1 Inference System of Trusted Time Server7.3.2 Security Features7.3.3 Synchronization with a Trusted Time Server7.3.4 Transit Addresses7.4 Test Methodology and Environment7.4.1 TTS Server and Data Collection for Inference7.4.2 Heterogeneous Network EnvironmentSimulation Case 1:Simulation Case 2:7.4.3 Graph-based Representation7.5 Case Study7.6 ConclusionNotesReferences
Chapter 8: Early Identification of Mental Health Disorder Employing Machine Learning-based Secure Edge Analytics: A Real-time Monitoring System
8.1 Introduction8.2 Traditional Methods Implemented in Edge Computing8.3 Secure Analytics of Smart Healthcare at the Edge8.4 Related Work: Overview of Mobile Applications for Mental Health8.4.1 Anxiety Reliever8.4.2 Anxiety Coach8.4.3 Breath2Relax8.4.4 Happify8.4.5 Head Space8.4.6 Mindshift8.4.7 MoodKit8.4.8 Panic Relief8.4.9 PTSD Coach8.5 Methodologies for Automated Real-Time Mood Detection for Assessing Anxiety and Depression Levels in the Edge with Privacy-Preservation Capability8.5.1 Data Preparation and Pre-processing8.5.1.1 Identifying Optic Flow in Facial Regions8.5.2 Pre-processing and Noise Elimination of the Image Data8.5.3 Questionnaire Data Description8.5.4 Proposed Architecture8.5.5 Data Analysis Using AI Techniques8.5.6 Privacy Preservation of the Model8.5.6.1 Federated Learning8.5.7 Model Deployment on Edge Devices8.6 Experimental Results8.6.1 SqlLite Analysis8.6.2 Machine Learning Algorithm Analysis8.6.3 Federated Learning Analysis8.6.4 Comparative Analysis8.7 ConclusionReferences
Chapter 9: Harnessing Artificial Intelligence for Secure ECG Analytics at the Edge for Cardiac Arrhythmia Classification
9.1 Introduction9.2 Literature Review9.3 Dataset Preparation9.4 Methodology9.4.1 ECG Pre-processing Phase9.4.2 Heartbeat Segmentation Phase9.4.3 Feature Extraction Phase9.4.4 Learning/Classification Phase9.5 Experimental Setups, Results and Discussion9.5.1 Performance Indicators9.5.2 Results for Experimental Setup 19.5.3 Results for Experimental Setup 29.6 ConclusionReferences
Chapter 10: On Securing Electronic Healthcare Records Using Hyperledger Fabric Across the Network Edge
10.1 Introduction10.2 Existing Decentralized Security Methods: Can Blockchain Be Used At the Edge?10.2.1 Current EHR System in Canada10.2.2 Challenges with the Traditional EHR Systems10.2.3 Security Measures for Health Records10.3 Current Challenges Faced by the Healthcare Workers in Covid-19 Pandemic10.3.1 Importance and Role of Medical Records During Pandemic10.3.2 Challenges Faced by Doctors10.3.3 Understanding the Proposed Architecture Using COVID-19 Example10.4 Scalable Secure Management and Access Control of Electronic Health Records at the Edge10.4.1 The Importance of Integrating Blockchain and Edge Computing?10.4.2 Challenges10.5 Overview of Blockchain and Hyper Ledger Methodologies10.5.1 Blockchain10.5.2 Electronic Health Records (EHRs)10.5.3 Smart Contract10.5.4 Access Control in Medical Domain10.5.5 Hyperledger10.5.6 Composer Tools10.5.7 Playground10.5.8 Off-chain Storage10.5.9 User Experience From Patient’s Side10.6 Hyper Ledger-Based Proposed Architecture for Protecting Electronic Health Records10.6.1 Proposed Architecture of the Blockchain System10.6.2 Data Flow Diagrams10.6.2.1 Doctors10.6.2.2 Patient10.6.2.3 Transaction Flow10.7 Performance Evaluation10.7.1 Performance of the Proposed Model10.7.2 Performance Comparison10.8 Conclusions and Future CaveatsReferences
Chapter 11: AI-Aided Secured ECG Live Edge Monitoring System with a Practical Use-Case
11.1 Introduction11.1.1 Background11.1.2 Problem Statement11.1.3 Objective and Scope11.2 Related Work11.3 Proposed AI-Based System Architecture11.3.1 Block Diagram11.3.2 Data Collection and Pre-Processing Steps11.3.3 Detecting Heart Abnormalities Using AI-Aided Techniques11.4 Considered Smart ECG Monitoring System11.4.1 Edge Hardware Components11.4.1.1 System-on-a-Chip (SoC) Model11.4.1.2 IoT Sensor for Heart Rate Data Acquisition11.4.1.3 Microprocessor and Analog to Digital Converter11.4.2 AI-Logic Component11.4.2.1 Decision Tree11.4.2.2 Random Forest11.4.2.3 ANN11.4.2.4 CNN11.5 Bio-Authentication Application of the Considered ECG Monitoring System for Specific Use-Cases11.6 Performance Evaluation11.6.1 Supraventricular Arrhythmia Classification11.6.2 Authorized User Classification for Bio-Authentication System11.7 Challenges Involved with the Proposed System11.8 Conclusion and Future ScopeReferences
Section III
Chapter 12: Application of Unmanned Aerial Vehicles in Wireless Networks: Mobile Edge Computing and Caching
12.1 Introduction12.1.1 Chapter Roadmap12.2 Literature Review12.3 Description of Caching and Mobile Edge Computing12.3.1 Overview of Caching12.3.1.1 Advantages12.3.1.2 Disadvantages12.3.2 Overview of Mobile Edge Computing12.3.2.1 Advantages12.3.2.2 Disadvantages12.4 Layering of UAV-Based MEC Architecture12.4.1 Explanation of the Layers12.5 System Model12.5.1 Mathematical Model of NOMA12.5.2 Path Loss Model12.5.3 Transmission Delay12.5.4 Computing Model12.5.4.1 Edge Computing Model12.5.4.2 Local Computing Model12.5.5 Time Consumption Model12.5.6 Energy Consumption Model12.6 Simulation Results12.7 ConclusionReferences
Chapter 13: Vehicular Edge Computing Security
13.1 Introduction13.1.1 Chapter Roadmap13.2 Vehicular Edge Computing Overview13.2.1 VEC Architecture and Provided Services13.2.2 Enabling Technologies: 5G, SDN, NFV, AI, Blockchain13.2.3 Overview of Challenges13.2.3.1 Task Offloading13.2.3.2 Network Management13.2.3.3 Caching13.2.3.4 Data Management13.2.3.5 Security and Privacy13.3 Security Threats and Analysis of Potential Security Challenges13.3.1 Access Control and Trust Management13.3.2 Data Management13.3.3 Decentralized Computation13.3.4 Intrusion and Anomaly Detection13.4 State-of-the-Art Solutions for Security Issues in VEC13.4.1 Identity Preservation, Trust Management and Authentication13.4.1.1 Pseudonym Management Scheme for Identity Preservation13.4.1.2 Ensuring Trust with Distributed Reputation Management13.4.1.3 Blockchain-Aided Cooperative Authentication13.4.2 Blockchain-Based Secure Data Management13.4.3 Secure Distributed Computation Techniques13.4.4 RSU Misbehavior and Vehicle Anomaly Detection13.5 Discussion13.6 ConclusionReferences
Section IV
Chapter 14: On Exploiting Blockchain Technology to Approach toward Secured, Sliced, and Edge Deployed Virtual Network Functions for Improvised IoT Services
14.1 Introduction14.2 Literature Review14.3 Blockchain-Powered Secured Slicing14.4 The Blockchain-Inspired Architecture for Network Slicing14.5 The Hyperledger Fabric-Driven Prototype14.6 ConclusionReferences
Chapter 15: Usage of Blockchain for Edge Computing
15.1 Introduction15.2 Applications and Benefits of Edge Computing15.2.1 Identify the Benefits of Using Edge Computing from Different Perspectives15.2.2 Identify the Applications of Edge Computing in Different Fields15.3 Issues in Edge Computing15.3.1 Issues in Security and Privacy15.3.2 Issues in Decentralized Architecture15.4 Integrating Blockchain in Edge Computing: The Missing Piece of the Puzzle?15.4.1 Blockchain: Beyond Cryptocurrency15.4.2 Advantages of Blockchain15.4.3 How Blockchain Will Complement Edge Computing15.4.4 How Blockchain Can be Integrated with Edge Computing15.4.4.1 Requirements: Integrated Blockchain and Edge Computing15.4.4.2 Overview on Existing Frameworks15.5 Challenges and Future Scope for Incorporating Blockchain to Edge Computing15.6 ConclusionReferences
Index

Content preview from Secure Edge Computing

Preface

The Internet is making our daily life increasingly digital, introducing a new era – the Internet of Everything (IoE). Edge computing is an emerging data analytics concept to address the challenges associated with IoE. More specifically, edge computing facilitates data analysis at the edge of the network instead of interacting with cloud-based servers. The consequence of this is that more devices will be added in remote locations without any overarching monitoring strategy. The increased connectivity and devices used for edge computing will create more opportunity for cyber criminals to exploit the system and its vulnerabilities.

Ensuring cyber security at the edge is a significant challenge and addressing it should not be an afterthought. ...

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Publisher Resources

ISBN: 9781000427325

Cloud Computing

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Secure Edge Computing

by Mohiuddin Ahmed, Paul Haskell-Dowland

Preface

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.