Оглавление
Группа авторов. The Smart Cyber Ecosystem for Sustainable Development
Table of Contents
List of Figures
List of Table
Guide
Pages
The Smart Cyber Ecosystem for Sustainable Development
Preface
1. Voyage of Internet of Things in the Ocean of Technology
1.1 Introduction
1.1.1 Characteristics of IoT
1.1.2 IoT Architecture
1.1.3 Merits and Demerits of IoT
1.2 Technological Evolution Toward IoT
1.3 IoT-Associated Technology
1.4 Interoperability in IoT
1.5 Programming Technologies in IoT
1.5.1 Arduino
1.5.2 Raspberry Pi
1.5.3 Python
1.6 IoT Applications
Conclusion
References
2. AI for Wireless Network Optimization: Challenges and Opportunities
2.1 Introduction to AI
2.2 Self-Organizing Networks
2.2.1 Operation Principle of Self-Organizing Networks
2.2.2 Self-Configuration
2.2.3 Self-Optimization
2.2.4 Self-Healing
2.2.5 Key Performance Indicators
2.2.6 SON Functions
2.3 Cognitive Networks
2.4 Introduction to Machine Learning
2.4.1 ML Types
2.4.2 Components of ML Algorithms
2.4.3 How do Machines Learn?
2.4.3.1 Supervised Learning
2.4.3.2 Unsupervised Learning
2.4.3.3 Semi-Supervised Learning
2.4.3.4 Reinforcement Learning
2.4.4 ML and Wireless Networks
2.5 Software-Defined Networks
2.5.1 SDN Architecture
2.5.2 The OpenFlow Protocol
2.5.3 SDN and ML
2.6 Cognitive Radio Networks
2.6.1 Sensing Methods
2.7 ML for Wireless Networks: Challenges and Solution Approaches
2.7.1 Cellular Networks. 2.7.1.1 Energy Saving
2.7.1.2 Channel Access and Assignment
2.7.1.3 User Association and Load Balancing
2.7.1.4 Traffic Engineering
2.7.1.5 QoS/QoE Prediction
2.7.1.6 Security
2.7.2 Wireless Local Area Networks
2.7.2.1 Access Point Selection
2.7.2.2 Interference Mitigation
2.7.2.3 Channel Allocation and Channel Bonding
2.7.2.4 Latency Estimation and Frame Length Selection
2.7.2.5 Handover
2.7.3 Cognitive Radio Networks
References
3. An Overview on Internet of Things (IoT) Segments and Technologies
3.1 Introduction
3.2 Features of IoT
3.3 IoT Sensor Devices
3.4 IoT Architecture
3.5 Challenges and Issues in IoT
3.6 Future Opportunities in IoT
3.7 Discussion
3.8 Conclusion
References
4. The Technological Shift: AI in Big Data and IoT
4.1 Introduction
4.2 Artificial Intelligence
4.2.1 Machine Learning
4.2.2 Further Development in the Domain of Artificial Intelligence
4.2.3 Programming Languages for Artificial Intelligence
4.2.4 Outcomes of Artificial Intelligence
4.3 Big Data
4.3.1 Artificial Intelligence Methods for Big Data
4.3.2 Industry Perspective of Big Data
4.3.2.1 In Medical Field
4.3.2.2 In Meteorological Department
4.3.2.3 In Industrial/Corporate Applications and Analytics
4.3.2.4 In Education
4.3.2.5 In Astronomy
4.4 Internet of Things
4.4.1 Interconnection of IoT With AoT
4.4.2 Difference Between IIoT and IoT
4.4.3 Industrial Approach for IoT
4.5 Technical Shift in AI, Big Data, and IoT
4.5.1 Industries Shifting to AI-Enabled Big Data Analytics
4.5.2 Industries Shifting to AI-Powered IoT Devices
4.5.3 Statistical Data of These Shifts
4.6 Conclusion
References
5. IoT’s Data Processing Using Spark
5.1 Introduction
5.2 Introduction to Apache Spark
5.2.1 Advantages of Apache Spark
5.2.2 Apache Spark’s Components
5.3 Apache Hadoop MapReduce
5.3.1 Limitations of MapReduce
5.4 Resilient Distributed Dataset (RDD)
5.4.1 Features and Limitations of RDDs
5.5 DataFrames
5.6 Datasets
5.7 Introduction to Spark SQL
5.7.1 Spark SQL Architecture
5.7.2 Spark SQL Libraries
5.8 SQL Context Class in Spark
5.9 Creating DataFrames
5.9.1 Operations on DataFrames
5.10 Aggregations
5.11 Running SQL Queries on DataFrames
5.12 Integration With RDDs
5.12.1 Inferring the Schema Using Reflection
5.12.2 Specifying the Schema Programmatically
5.13 Data Sources
5.13.1 JSON Datasets
5.13.2 Hive Tables
5.13.3 Parquet Files
5.14 Operations on Data Sources
5.15 Industrial Applications
5.16 Conclusion
References
6. SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs
6.1 Introduction
6.1.1 Components of WSNs
6.1.2 Trust
6.1.3 Major Contribution
6.2 Related Work
6.3 Network Topology and Assumptions
6.4 Proposed Trust Model
6.4.1 CM to CM (Direct) Trust Evaluation Scheme
6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx,y(∆t))
6.4.3 CH-to-CH Direct Trust Estimation
6.4.4 BS-to-CH Feedback Trust Calculation
6.5 Result and Analysis
6.5.1 Severity Analysis
6.5.2 Malicious Node Detection
6.6 Conclusion and Future Work
References
7. Smart Applications of IoT
7.1 Introduction
7.2 Background. 7.2.1 Enabling Technologies for Building Intelligent Infrastructure
7.3 Smart City
7.3.1 Benefits of a Smart City
7.3.2 Smart City Ecosystem
7.3.3 Challenges in Smart Cities
7.4 Smart Healthcare
7.4.1 Smart Healthcare Applications
7.4.2 Challenges in Healthcare
7.5 Smart Agriculture
7.5.1 Environment Agriculture Controlling
7.5.2 Advantages
7.5.3 Challenges
7.6 Smart Industries
7.6.1 Advantages
7.6.2 Challenges
7.7 Future Research Directions
7.8 Conclusions
References
8. Sensor-Based Irrigation System: Introducing Technology in Agriculture
8.1 Introduction
8.1.1 Technology in Agriculture
8.1.2 Use and Need for Low-Cost Technology in Agriculture
8.2 Proposed System
8.3 Flow Chart
8.4 Use Case
8.5 System Modules. 8.5.1 Raspberry Pi
8.5.2 Arduino Uno
8.5.3 DHT 11 Humidity and Temperature Sensor
8.5.4 Soil Moisture Sensor
8.5.5 Solenoid Valve
8.5.6 Drip Irrigation Kit
8.5.7 433 MHz RF Module
8.5.8 Mobile Application
8.5.9 Testing Phase
8.6 Limitations
8.7 Suggestions
8.8 Future Scope
8.9 Conclusion
Acknowledgement
References
Suggested Additional Readings
Key Terms and Definitions
Appendix
Example Code
9. Artificial Intelligence: An Imaginary World of Machine
9.1 The Dawn of Artificial Intelligence
9.2 Introduction
9.3 Components of AI
9.3.1 Machine Reasoning
9.3.2 Natural Language Processing
9.3.3 Automated Planning
9.3.4 Machine Learning
9.4 Types of Artificial Intelligence
9.4.1 Artificial Narrow Intelligence
9.4.2 Artificial General Intelligence
9.4.3 Artificial Super Intelligence
9.5 Application Area of AI
9.6 Challenges in Artificial Intelligence
9.7 Future Trends in Artificial Intelligence
9.8 Practical Implementation of AI Application
References
10. Impact of Deep Learning Techniques in IoT
10.1 Introduction
10.2 Internet of Things
10.2.1 Characteristics of IoT
10.2.2 Architecture of IoT
10.2.2.1 Smart Device/Sensor Layer
10.2.2.2 Gateways and Networks
10.2.2.3 Management Service Layer
10.2.2.4 Application Layer
10.2.2.5 Interoperability of IoT
10.2.2.5.1 Types of Interoperability
10.2.2.5.1.1 TECHNICAL INTEROPERABILITY
10.2.2.5.1.2 SYNTACTICAL INTEROPERABILITY
10.2.2.5.1.3 SEMANTIC INTEROPERABILITY
10.2.2.5.1.4 ORGANIZATIONAL INTEROPERABILITY
10.2.2.6 Security Requirements at a Different Layer of IoT
10.2.2.6.1 Application Layer
10.2.2.6.2 Service Support Layer
10.2.2.6.3 Network Layer
10.2.2.6.4 Smart Object/Sensor
10.2.2.7 Future Challenges for IoT
10.2.2.8 Privacy and Security
10.2.2.9 Cost and Usability
10.2.2.10 Data Management
10.2.2.11 Energy Preservation
10.2.2.12 Applications of IoT
10.2.2.12.1 Smart Living
10.2.2.12.2 Smart Cities
10.2.2.12.3 Smart Environment
10.2.2.12.4 Smart Industry
10.2.2.12.5 Smart Energy
10.2.2.12.6 Smart Agriculture
10.2.2.13 Essential IoT Technologies
10.2.2.13.1 Radio Frequency Identification
10.2.2.13.2 Wireless Sensor Networks
10.2.2.13.3 Middleware
10.2.2.13.4 Cloud Computing
10.2.2.13.5 IoT Application Software
10.2.2.14 Enriching the Customer Value
10.2.2.14.1 Monitoring and Control
10.2.2.14.2 Big Data and Business Analytics
10.2.2.14.3 Information Sharing and Collaboration
10.2.2.15 Evolution of the Foundational IoT Technologies
10.2.2.16 Technical Challenges in the IoT Environment
10.2.2.16.1 Data Management Challenge
10.2.2.16.2 Data Mining Challenge
10.2.2.16.3 Privacy Challenge
10.2.2.17 Security Challenge
10.2.2.18 Chaos Challenge
10.2.2.19 Advantages of IoT
10.2.2.20 Disadvantages of IoT
10.3 Deep Learning
10.3.1 Models of Deep Learning
10.3.1.1 Convolutional Neural Network
10.3.1.2 Recurrent Neural Networks
10.3.1.3 Long Short-Term Memory
10.3.1.4 Autoencoders
10.3.1.5 Variational Autoencoders
10.3.1.6 Generative Adversarial Networks
10.3.1.7 Restricted Boltzmann Machine
10.3.1.8 Deep Belief Network
10.3.1.9 Ladder Networks
10.3.2 Applications of Deep Learning
10.3.2.1 Industrial Robotics
10.3.2.2 E-Commerce Industries
10.3.2.3 Self-Driving Cars
10.3.2.4 Voice-Activated Assistants
10.3.2.5 Automatic Machine Translation
10.3.2.6 Automatic Handwriting Translation
10.3.2.7 Predicting Earthquakes
10.3.2.8 Object Classification in Photographs
10.3.2.9 Automatic Game Playing
10.3.2.10 Adding Sound to Silent Movies
10.3.3 Advantages of Deep Learning
10.3.4 Disadvantages of Deep Learning
10.3.5 Deployment of Deep Learning in IoT
10.3.6 Deep Learning Applications in IoT
10.3.6.1 Image Recognition
10.3.6.2 Speech/Voice Recognition
10.3.6.3 Indoor Localization
10.3.6.4 Physiological and Psychological Detection
10.3.6.5 Security and Privacy
10.3.7 Deep Learning Techniques on IoT Devices
10.3.7.1 Network Compression
10.3.7.2 Approximate Computing
10.3.7.3 Accelerators
10.3.7.4 Tiny Motes
10.4 IoT Challenges on Deep Learning and Future Directions
10.4.1 Lack of IoT Dataset
10.4.2 Pre-Processing
10.4.3 Challenges of 6V’s
10.4.4 Deep Learning Limitations
10.5 Future Directions of Deep Learning
10.5.1 IoT Mobile Data
10.5.2 Integrating Contextual Information
10.5.3 Online Resource Provisioning for IoT Analytics
10.5.4 Semi-Supervised Analytic Framework
10.5.5 Dependable and Reliable IoT Analytics
10.5.6 Self-Organizing Communication Networks
10.5.7 Emerging IoT Applications
10.5.7.1 Unmanned Aerial Vehicles
10.5.7.2 Virtual/Augmented Reality
10.5.7.3 Mobile Robotics
10.6 Common Datasets for Deep Learning in IoT
10.7 Discussion
10.8 Conclusion
References
11. Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques
11.1 Introduction
11.2 Existing Methods Review
11.3 Methodology
11.3.1 Architecture of Stride U-Net
11.3.2 Loss Function
11.4 Databases and Evaluation Metrics
11.4.1 CNN Implementation Details
11.5 Results and Analysis. 11.5.1 Evaluation on DRIVE and STARE Databases
11.5.2 Comparative Analysis
11.6 Concluding Remarks
References
12. Existing Trends in Mental Health Based on IoT Applications: A Systematic Review
12.1 Introduction
12.2 Methodology
12.3 IoT in Mental Health
12.4 Mental Healthcare Applications and Services Based on IoT
12.5 Benefits of IoT in Mental Health. 12.5.1 Reduction in Treatment Cost
12.5.2 Reduce Human Error
12.5.3 Remove Geographical Barriers
12.5.4 Less Paperwork and Documentation
12.5.5 Early Stage Detection of Chronic Disorders
12.5.6 Improved Drug Management
12.5.7 Speedy Medical Attention
12.5.8 Reliable Results of Treatment
12.6 Challenges in IoT-Based Mental Healthcare Applications. 12.6.1 Scalability
12.6.2 Trust
12.6.3 Security and Privacy Issues
12.6.4 Interoperability Issues
12.6.5 Computational Limits
12.6.6 Memory Limitations
12.6.7 Communications Media
12.6.8 Devices Multiplicity
12.6.9 Standardization
12.6.10 IoT-Based Healthcare Platforms
12.6.11 Network Type
12.6.12 Quality of Service
12.7 Blockchain in IoT for Healthcare
12.8 Results and Discussion
12.9 Limitations of the Survey
12.10 Conclusion
References
13. Monitoring Technologies for Precision Health
13.1 Introduction
13.2 Applications of Monitoring Technologies
13.2.1 Everyday Life Activities
13.2.2 Sleeping and Stress
13.2.3 Breathing Patterns and Respiration
13.2.4 Energy and Caloric Consumption
13.2.5 Diabetes, Cardiac, and Cognitive Care
13.2.6 Disability and Rehabilitation
13.2.7 Pregnancy and Post-Procedural Care
13.3 Limitations
13.3.1 Quality of Data and Reliability
13.3.2 Safety, Privacy, and Legal Concerns
13.4 Future Insights. 13.4.1 Consolidating Frameworks
13.4.2 Monitoring and Intervention
13.4.3 Research and Development
13.5 Conclusions
References
14. Impact of Artificial Intelligence in Cardiovascular Disease
14.1 Artificial Intelligence
14.2 Machine Learning
14.3 The Application of AI in CVD
14.3.1 Precision Medicine
14.3.2 Clinical Prediction
14.3.3 Cardiac Imaging Analysis
14.4 Future Prospect
14.5 PUAI and Novel Medical Mode. 14.5.1 Phenomenon of PUAI
14.5.2 Novel Medical Model
14.6 Traditional Mode. 14.6.1 Novel Medical Mode Plus PUAI
14.7 Representative Calculations of AI
14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis
References
15. Healthcare Transformation With Clinical Big Data Predictive Analytics
15.1 Introduction
15.1.1 Big Data in Health Sector
15.1.2 Data Structure Produced in Health Sectors
15.2 Big Data Challenges in Healthcare
15.2.1 Big Data in Computational Healthcare
15.2.2 Big Data Predictive Analytics in Healthcare
15.2.3 Big Data for Adapted Healthcare
15.3 Cloud Computing and Big Data in Healthcare
15.4 Big Data Healthcare and IoT
15.5 Wearable Devices for Patient Health Monitoring
15.6 Big Data and Industry 4.0
15.7 Conclusion
References
16. Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats
16.1 Introduction. 16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate
16.1.2 Precautionary Guidelines Followed in Indian Continent
16.1.3 Spiritual Guidelines in Indian Society. 16.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India
16.1.4 Veda Vigyaan: Ancient Vedic Knowledge
16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon
16.1.6 The Yagya Samagri
16.2 Literature Survey. 16.2.1 Technical Aspects of Yajna and Mantra Therapy
16.2.2 Mantra Chanting and Its Science
16.2.3 Yagya Medicine (Yagyopathy)
16.2.4 The Medicinal HavanSamagri Components. 16.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases
16.2.5 Scientific Benefits of Havan
16.3 Experimental Setup Protocols With Results
16.3.1 Subject Sample Distribution
16.3.1.1 Area Wise Distribution
16.3.2 Conclusion and Discussion Through Experimental Work
16.4 Future Scope and Limitations
16.5 Novelty
16.6 Recommendations
16.7 Applications of Yajna Therapy
16.8 Conclusions
Acknowledgement
References
Key Terms and Definitions
17. Extraction of Depression Symptoms From Social Networks
17.1 Introduction
17.1.1 Diagnosis and Treatments
17.2 Data Mining in Healthcare
17.2.1 Text Mining
17.3 Social Network Sites
17.4 Symptom Extraction Tool
17.4.1 Data Collection
17.4.2 Data Processing
17.4.3 Data Analysis
17.5 Sentiment Analysis
17.5.1 Emotion Analysis
17.5.2 Behavioral Analysis
17.6 Conclusion
References
18. Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations
18.1 Introduction
18.2 Characteristics of Fog Computing
18.3 Reference Architecture of Fog Computing
18.4 CISCO IOx Framework
18.5 Security Practices in CISCO IOx
18.5.1 Potential Attacks on IoT Architecture
18.5.2 Perception Layer (Sensing)
18.5.3 Network Layer
18.5.4 Service Layer (Support)
18.5.5 Application Layer (Interface)
18.6 Security Issues in Fog Computing
18.6.1 Virtualization Issues
18.6.2 Web Security Issues
18.6.3 Internal/External Communication Issues
18.6.4 Data Security Related Issues
18.6.5 Wireless Security Issues
18.6.6 Malware Protection
18.7 Machine Learning for Secure Fog Computing
18.7.1 Layer 1 Cloud
18.7.2 Layer 2 Fog Nodes For The Community
18.7.3 Layer 3 Fog Node for Their Neighborhood
18.7.4 Layer 4 Sensors
18.8 Existing Security Solution in Fog Computing
18.8.1 Privacy-Preserving in Fog Computing
18.8.2 Pseudocode for Privacy Preserving in Fog Computing
18.8.3 Pseudocode for Feature Extraction
18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature
18.8.5 Pseudocode for Encrypting Data
18.8.6 Pseudocode for Data Partitioning
18.8.7 Encryption Algorithms in Fog Computing
18.9 Recommendation and Future Enhancement
18.9.1 Data Encryption
18.9.2 Preventing from Cache Attacks
18.9.3 Network Monitoring
18.9.4 Malware Protection
18.9.5 Wireless Security
18.9.6 Secured Vehicular Network
18.9.7 Secure Multi-Tenancy
18.9.8 Backup and Recovery
18.9.9 Security with Performance
18.10 Conclusion
References
19. Cybersecurity and Privacy Fundamentals
19.1 Introduction
19.2 Historical Background and Evolution of Cyber Crime
19.3 Introduction to Cybersecurity
19.3.1 Application Security
19.3.2 Information Security
19.3.3 Recovery From Failure or Disaster
19.3.4 Network Security
19.4 Classification of Cyber Crimes
19.4.1 Internal Attacks
19.4.2 External Attacks
19.4.3 Unstructured Attack
19.4.4 Structured Attack
19.5 Reasons Behind Cyber Crime
19.5.1 Making Money
19.5.2 Gaining Financial Growth and Reputation
19.5.3 Revenge
19.5.4 For Making Fun
19.5.5 To Recognize
19.5.6 Business Analysis and Decision Making
19.6 Various Types of Cyber Crime
19.6.1 Cyber Stalking
19.6.2 Sexual Harassment or Child Pornography
19.6.3 Forgery
19.6.4 Crime Related to Privacy of Software and Network Resources
19.6.5 Cyber Terrorism
19.6.6 Phishing, Vishing, and Smishing
19.6.7 Malfunction
19.6.8 Server Hacking
19.6.9 Spreading Virus
19.6.10 Spamming, Cross Site Scripting, and Web Jacking
19.7 Various Types of Cyber Attacks in Information Security
19.7.1 Web-Based Attacks in Information Security
19.7.2 System-Based Attacks in Information Security
19.8 Cybersecurity and Privacy Techniques
19.8.1 Authentication and Authorization
19.8.2 Cryptography
19.8.2.1 Symmetric Key Encryption
19.8.2.2 Asymmetric Key Encryption
19.8.3 Installation of Antivirus
19.8.4 Digital Signature
19.8.5 Firewall
19.8.6 Steganography
19.9 Essential Elements of Cybersecurity
19.10 Basic Security Concerns for Cybersecurity
19.10.1 Precaution
19.10.2 Maintenance
19.10.3 Reactions
19.11 Cybersecurity Layered Stack
19.12 Basic Security and Privacy Check List
19.13 Future Challenges of Cybersecurity
References
20. Changing the Conventional Banking System through Blockchain
20.1 Introduction. 20.1.1 Introduction to Blockchain
20.1.2 Classification of Blockchains
20.1.2.1 Public Blockchain
20.1.2.2 Private Blockchain
20.1.2.3 Hybrid Blockchain
20.1.2.4 Consortium Blockchain
20.1.3 Need for Blockchain Technology
20.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary
20.1.3.1.1 Outcomes
20.1.3.1.1.1 BITCOIN TRANSACTIONS
20.1.3.1.1.2 CREDIT CARD TRANSACTIONS
20.1.3.1.1.3 KEY DIFFERENCES
20.1.4 Comparison of Blockchain and Cryptocurrency
20.1.4.1 Distributed Ledger Technology (DLT)
20.1.5 Types of Consensus Mechanism
20.1.5.1 Consensus Algorithm: A Quick Background
20.1.6 Proof of Work
20.1.7 Proof of Stake
20.1.7.1 Delegated Proof of Stake
20.1.7.2 Byzantine Fault Tolerance
20.2 Literature Survey. 20.2.1 The History of Blockchain Technology
20.2.2 Early Years of Blockchain Technology: 1991–2008
20.2.2.1 Evolution of Blockchain: Phase 1—Transactions. 20.2.2.1.1 Blockchain Version 1.0: 2008-2013: Bitcoin Emergence
20.2.2.2 Evolution of Blockchain: Phase 2—Contracts. 20.2.2.2.1 Blockchain Version 2.0:2013-15 Ethereum Development
20.2.2.3 Evolution of Blockchain: Phase 3—Applications. 20.2.2.3.1 Blockchain Version 3.0:2018: The Future Scope
20.2.3 Literature Review
20.2.4 Analysis
20.3 Methodology and Tools. 20.3.1 Methodology
20.3.2 Flow Chart
20.3.3 Tools and Configuration
20.4 Experiment. 20.4.1 Steps of Implementation
20.4.2 Screenshots of Experiment
20.5 Results
20.6 Conclusion
20.7 Future Scope
20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises
References
21. A Secured Online Voting System by Using Blockchain as the Medium
21.1 Blockchain-Based Online Voting System. 21.1.1 Introduction
21.1.2 Structure of a Block in a Blockchain System
21.1.3 Function of Segments in a Block of the Blockchain
21.1.4 SHA-256 Hashing on the Blockchain
21.1.5 Interaction Involved in Blockchain-Based Online Voting System
21.1.6 Online Voting System Using Blockchain – Framework
21.2 Literature Review. 21.2.1 Literature Review Outline
21.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model
21.2.1.2 Online Voting System Based on Visual Cryptography
21.2.1.3 Online Voting System Using Biometric Security and Steganography
21.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption
21.2.1.5 An Online Voting System Based on a Secured Blockchain
21.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach
21.2.1.7 Online Voting System Using Iris Recognition
21.2.1.8 Online Voting System Based on NID and SIM
21.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography
21.2.1.10 Online Voting System Using Secret Sharing–Based Authentication
21.2.2 Comparing the Existing Online Voting System
References
22. Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects
22.1 Introduction
22.2 Literature Review
22.3 Different Variants of Cybersecurity in Action
22.4 Importance of Cybersecurity in Action
22.5 Methods for Establishing a Strategy for Cybersecurity
22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity
22.7 Where AI Is Actually Required to Deal With Cybersecurity
22.8 Challenges for Cybersecurity in Current State of Practice
22.9 Conclusion
References
Index
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