Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
About the Editors
1. Securing Dedicated DSP Co-processors (Hardware IP) using Structural Obfuscation for IoT-oriented Platforms
1.1 Introduction
1.2 Discussion on Contemporary Structural Obfuscation Approaches used for Securing DSP Hardware/Coprocessor
1.2.1 Securing DSP Designs Using Compiler Driven Transformation Based Structural Obfuscation
1.2.2 Enhanced Security of DSP Circuits Using Multi-key Based Structural Obfuscation
1.2.3 Securing DSP Kernels Using Robust Hologram Based Obfuscation
Overview
Demonstration
1.2.4 Securing DSP Designs Using HLT Based Structural Obfuscation
1.3 Analysis of Case Studies
1.3.1 Design Analysis
1.3.2 Security Analysis
1.4 Conclusion
References
2. Multi-bit True Random Number Generator for IoT Devices using Memristor
2.1 Introduction
2.2 Background and Related Work
2.2.1 TRNGs and Statistical Randomness Testing
2.2.2 Memristors and Memristor based TRNGs
2.2.3 Related Works
2.3 Proposed Multi-bit Random Number Generator
2.3.1 TRNG Architecture without Memristor
2.3.2 Bit Correlation Effect
2.4 Experimental Results
2.4.1 Simulation Setup Details
2.4.2 Statistical Randomness Testing Results
2.4.3 Entropy Calculation
2.5 Comparison with Existing Memristor Based TRNGs
2.6 Conclusion
References
3. Secured Testing of AES Cryptographic ICs for IoT Devices
3.1 Introduction
3.2 Cryptography for Security in IoT Devices
3.3 Advanced Encryption Standard (AES) Algorithm for Security in IoT Devices
3.4 Scan-based Side-channel Attack on AES Cryptographic ICs
3.5 Design and Simulation of Scan-inserted AES Crypto Module
3.5.1 Design of AES
3.5.2 Design and Simulation of Scan-inserted AES Design
3.6 Enhanced Protection of AES Crypto Module Scan Chain Structure: A Case Study
3.6.1 XOR Based Obfuscation Technique
3.6.2 Hybrid Obfuscation for the Scan Output
3.7 Results Analysis
3.7.1 XOR-based Obfuscation and SET Attack
3.7.2 Hybrid Obfuscation and SET/RESET attack
3.7.3 Signature Attack
3.7.4 Impact on Testability
3.8 Conclusions
Acknowledgment
References
4. Biometric-based Secure Authentication for IoT-enabled Devices and Applications
4.1 Internet-of-Things (IoT) Impacting our Livelihood
4.2 IoT Ecosystem
4.3 Classification of IoT-powered Applications and Services
4.4 IoT Security Breach
4.5 Current Scenario of Security in IoT Infrastructure
4.6 IoT Threat Model and Mitigation Approaches
4.7 Authentication Using Biometric Systems
4.8 Authentication in IoT System
4.9 Biometrics for IoT Security
4.10 Conclusion
References
5. An Improved Verification Scheme Based on User Biometrics
5.1 Introduction
5.2 Working of the Hardware (Biometric Sensor)
5.3 Literature Survey
5.4 Previous System
5.5 Notation Employed in the Proposal
5.6 Assumptions of the Proposed System
5.7 Proposed System
5.8 Security Analysis
5.9 Conclusion
References
6. Obfuscation to Mitigate Hardware Attacks in Edge Nodes of IoT System
6.1 Introduction to Hardware Security in IoT Systems
6.2 Chapter Organization
6.2.1 The Origin of Hardware Security
6.2.2 Types of Security Attacks in IoT
6.2.2.1 Physical Attacks
6.2.3 Classification of Physical or Hardware Attacks in IoT Systems
6.2.4 The Consequences of Security Attacks
6.2.5 The Challenges of Securing the IoT nodes
6.3 Major Contribution
6.3.1 Folding Transformation
6.3.2 Register Minimization Technique
6.3.3 Obfuscation through High-level Transformation
6.3.4 Variation of Modes to Increase Security Level
6.3.5 Methodology Adapted for Obfuscating DSP Circuit
6.3.6 Salient Features of Hardware Security via Obfuscation
6.3.7 Hardware Implementation of Obfuscated DSP Circuit
6.3.8 Filter Design using FDA Tool
6.3.9 Biquad Filter Implementation using System Generator
6.3.10 Folded Biquad Filter Implementation with and without Register Minimization using System Generator
6.3.11 Verilog HDL Implementation of Folded Biquad Filter Implementation with Register Minimization
6.3.12 Comparison of Various Methods of Implementation
6.3.13 Implementation of Obfuscated Design via High Level Transformation
6.3.14 Xilinx Vivado Implementation of Obfuscated Folded Biquad Filter
6.4 Leveraging New Technologies to Mitigate Hardware Attack in IoT Nodes
6.4.1 Artificial Intelligence (AI) Technology
6.4.2 ML based Hardware Security for IoT Devices
6.4.2.1 ML based Hardware Trojan Detection
6.4.2.2 ML based Side-Channel Analysis (SCA)
6.4.2.3 ML in System on Chip (SoC) Architecture
6.5 Conclusion and Future Scope
References
7. Lightweight Security Solutions for IoT using Physical-Layer Key Generation
7.1 Introduction
7.2 Motivation
7.3 Wireless Security
7.4 Physical-layer Key Generation
7.4.1 Wiretap Channel Model
7.4.2 Principles of Key Generation
7.4.2.1 Temporal Variation
7.4.2.2 Channel Reciprocity
7.4.2.3 Spatial Decorrelation
7.4.3 Performance Metrics
7.4.3.1 Bit Disagreement Rate (BDR)
7.4.3.2 Key Randomness
7.4.4 Key Generation Procedure
7.4.4.1 Channel Probing
7.4.4.2 Quantization
7.4.4.3 Information Reconciliation
7.4.4.4 Privacy Amplification
7.5 Applications and Future Scope
7.6 Conclusion
Acknowledgment
References
8. Threat and Attack Models in IoT Devices
8.1 Need for Security in IoT Devices
8.2 IoT Architecture
8.2.1 Challenges Facing by IoT Security
8.3 IoT Attacks Taxonomy
8.3.1 Software Attacks
8.3.2 Privacy of IoT
8.3.3 Privacy Threats
8.4 Attacks, Threats, and Vulnerabilities
8.4.1 Attacks on LayerโNetwork
8.4.2 Attacks on LayerโApplication Use
8.4.3 SpoofingโPhish Attack
8.4.4 Injection of Malware
8.4.5 Malicious Scripting Code
8.5 Design of Malware Attacks
8.5.1 Structure of Testbed
8.5.2 Module Interface
8.5.3 Computer Networking
8.5.4 Methodology: Automated Testbed Process
8.6 Impact of Attacks on Security Objectives
8.6.1 IoT Network Privacy Preservation Solutions
8.6.2 Application Layer Security
8.6.3 Protection on IoT
8.7 Conclusion
References
9. Review on Hardware Attacks and Security Challenges in IoT Edge Nodes
9.1 Introduction
9.2 IoT Edge Nodes Architecture
9.2.1 Specifications of IoT Nodes
9.3 Challenges in Security IoT Nodes
9.3.1 Security Taxonomy
9.4 Impact of Threats/Attacks on IoT Architecture
9.4.1 Hardware Trojan
9.4.2 Hardware Trojan Taxonomy
9.4.2.1 Physical
9.4.2.2 Insertion Phase
9.4.2.3 Activation
9.4.2.4 Payload
9.4.2.5 Threats
9.4.2.6 Location
9.5 Internet-of-Things Layer's Security Vulnerabilities
9.5.1 Perception Layer
9.5.1.1 Security Solutions to Perception Layer
9.5.2 Network Layer
9.5.2.1 Security Solutions for the Network Layer
9.5.3 Processing Layer
9.5.3.1 Security Solutions for the Processing Layer
9.5.4 Application Layer
9.5.4.1 Security Solutions for the Application Layer
9.6 Countermeasures
9.6.1 Trojan Detection
9.6.1.1 Pre-silicon Techniques
9.6.1.2 Post-silicon Techniques
9.6.2 Design for Trust
9.6.3 Prevention of Hardware Trojan Insertion
9.6.4 Split Manufacturing
9.6.5 Hardware Security Module
9.6.6 Trusted Platform Module
9.6.7 Physical Unclonable Functions
9.6.8 Device Identity
9.6.8.1 EPIC: Framework to Protect Smart Homes in IoT Environments
9.6.8.2 Static Random Access Memory-Physical Unclonable Function
9.6.8.3 SRPL [Secure Routing Protocol]
9.6.8.4 INTI [Intrusion Detection System]
9.6.8.5 ML-IDS [Machine Learning based Intrusion Detection System]
9.6.8.6 SecTrust
9.6.8.7 SMQTT [Secure Extension of MQTT (Message Queue Telemetry Transport)]
9.6.8.8 DDoS
9.6.8.9 Software Defined-IoT
9.6.8.10 Lightweight Algorithm
9.6.8.11 Defense Against Gray Hole Attacks in Edge Nodes
9.6.8.12 Defence Against Sinkhole and Rushing Attacks in Edge Nodes
9.7 Conclusion
References
10. Study of Hardware Attacks on Smart System Design Lab
10.1 Introduction
10.1.1 Basics of IoT Devices
10.2 The loT Architecture
10.2.1 Components of the loT Architecture
10.2.2 An IoT Platform
10.2.2.1 Types of IoT Platform
10.2.3 loT Edge Computing
10.2.3.1 Cloud Computing
10.2.3.2 The IoT Gateway
10.2.3.3 Artificial Intelligence
10.2.3.4 5G Networks
10.2.3.5 Types of Platform for IoT Edge Computing
10.2.3.6 The architecture of IoT Edge Computing
10.2.3.7 IoT Edge Devices for Now and the Future
10.3 Hardware and Software Components of IoT Applications
10.3.1 Smart Home
10.3.2 Smart Industry
10.3.2.1 Improving Efficiency
10.3.2.2 Increase Uptime
10.3.2.3 Improve Safety
10.3.2.4 Edge Device at the Front End
10.3.2.5 Connected Technology
10.3.2.6 IoT Platform for Data Analytics
10.3.3 Smart Agriculture
10.3.3.1 Components of Smart agriculture
10.3.3.2 Hardware
10.3.3.3 The Uses of AI
10.3.3.4 Device Maintenance
10.3.3.5 Flexibility
10.4 Hardware Security in IoT Edge Computing
10.5 Hardware Attacks
10.5.1 Invasive Attacks
10.5.1.1 Physical Attacks
10.5.1.2 Tampering
10.5.1.3 Micro-probing
10.5.1.4 Battery Draining
10.5.1.5 DOS Attacks
10.5.1.6 Cloning Attack
10.5.2 Non-Invasive
10.5.2.1 Side-channel Attacks
10.5.2.2 Communication-Signal-Based Attacks
10.5.2.3 Power-based Attacks
10.5.2.4 Embedded Sensor-based Attacks
10.5.2.5 IoT Malware Attack
10.5.2.6 Edge Server Attacks
10.5.2.7 Ransomware
10.5.2.8 Thingbots
10.5.2.9 Trojan Horse
10.5.3 Semi-Invasive
10.6 Countermeasures
10.6.1 Security Measures for IoT Devices
10.7 Case Study on Smart Lab
10.7.1 Automation of Smart Lab
10.7.1.1 Select an Academic Platform for Smart Lab
10.7.1.2 The Hardware and Software Requirements
10.7.1.3 Lab Monitoring and Control System
10.7.1.4 Attendance System
10.7.1.5 Lab Manual System
10.7.1.6 Kits usage Monitoring and Evaluation
10.7.1.7 Consider Progress in terms of Scalability
10.7.1.8 The Operation of the Application should be Extremely Fast
10.7.2 Simulation of System Design Lab
10.7.2.1 Lab Monitoring and Control System
10.7.2.2 Attendance System
10.7.3 Security Threat Analysis
10.8 Conclusion
References
11. A Novel Threat Modeling and Attack Analysis for IoT Applications
11.1 Introduction
11.1.1 Security in IoT Devices
11.1.2 Organization of the Chapter
11.2 Literature Survey
11.3 Terminology used in Proposed Threat Modeling for IoT Devices
11.3.1 Basic Terminology
11.3.1.1 CIA Trait
11.3.1.2 Vulnerabilities
11.3.1.3 Threat
11.3.1.4 Risk
11.3.1.5 Threat Modeling
11.3.2 Steps Involved in Threat Modeling
11.3.3 Determine the Scope
11.3.4 Identify and Prioritise Assets
11.3.5 Perform Decomposition Analysis
11.3.6 Realise Existing Controls
11.3.7 Identify, Classify and Prioritise Threats
11.3.8 Analyze the Hardware Situation
11.3.9 Prioritise to Respond
11.3.10 Experimental Results
11.4 Adopting the Proposed IoT-TMA for Various Applications
11.4.1 Smart Home Environment
11.4.1.1 Determine the Scope
11.4.1.2 Identify and Prioritise Assets
11.4.1.3 Perform Decomposition Analysis
11.4.1.4 Realise Existing Controls
11.4.1.5 Identify, Classify and Prioritise Threats
11.4.1.6 Analyze the Hardware Situation
11.4.1.7 Prioritise to Respond
11.4.2 IoT-based Garment Unit
11.4.2.1 Determine the Scope
11.4.2.2 Identify and Prioritise Assets
11.4.2.3 Perform Decomposition Analysis
11.4.2.4 Realise Existing Controls
11.4.2.5 Identify, Classify and Prioritise Threats
11.4.2.6 Analyze Hardware Situation
11.4.2.7 Prioritise to Respond
11.4.3 IoT-based Water Quality Monitoring System
11.4.3.1 Determine the Scope
11.4.3.2 Identify and Prioritise Assets
11.4.3.3 Perform Decomposition Analysis
11.4.3.4 Realise Existing Controls
11.4.3.5 Identify, Categorise and Prioritise Threats
11.4.3.6 Analyze the Hardware Situation
11.4.3.7 Prioritise to Respond
11.5 Mitigation Techniques for Threats in IoT Devices
11.5.1 Network Segmentation
11.5.2 Effective Encryption
11.5.3 Effective Patch Management
11.5.4 Disabling Unnecessary Features
11.5.5 Proper Physical Security
11.6 Conclusion
11.6.1 Future Work
References
12. Trust Management in Internet-of-Things Devices
12.1 Introduction
12.2 Fundamentals of the Trust Model Concept
12.3 Esteem Assets and Trust Management Goals
12.4 Objectives of Trust Management in Different Layers of IoT ()
12.5 Transport Systems Trust Management
12.6 Trust Management in P2P Networks
12.7 Trust Management in Social IoT
12.8 Trust Management Techniques in IoT
12.9 Issues and Challenges in Trust
12.10 Trust Applications
12.11 Conclusion
References
Index