Smart Cyber Ecosystem for Sustainable Development

Pardeep Kumar is a Professor and Head of the Software Engineering Department and Director ORIC, Quaid-e-Awam University of Engineering, Science & Technology (QUEST) Nawabshah, Pakistan. He completed his PhD from Berlin, Germany in 2012. He has authored more than 50 research publications in reputed journals and conferences around the world including three books and several book chapters. Vishal Jain PhD is an associate professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U.P. India. He has authored more than 85 research papers in reputed conferences and journals, and has authored and edited more than 10 books. Vasaki Ponnusamy is an assistant professor in the Universiti Tunku Abdul Rahman, Malaysia where she heads the Department of Computer and Communication Technology. She obtained her PhD in IT from Universiti Teknologi PETRONAS (UTP), Malaysia (2013).

• Preface xxiPart 1: Internet of Things 11 Voyage of Internet of Things in the Ocean of Technology 3Tejaskumar R. Ghadiyali, Bharat C. Patel and Manish M. Kayasth1.1 Introduction 31.1.1 Characteristics of IoT 41.1.2 IoT Architecture 51.1.3 Merits and Demerits of IoT 61.2 Technological Evolution Toward IoT 71.3 IoT-Associated Technology 81.4 Interoperability in IoT 141.5 Programming Technologies in IoT 151.5.1 Arduino 151.5.2 Raspberry Pi 171.5.3 Python 181.6 IoT Applications 19Conclusion 22References 222 AI for Wireless Network Optimization: Challenges and Opportunities 25Murad Abusubaih2.1 Introduction to AI 252.2 Self-Organizing Networks 272.2.1 Operation Principle of Self-Organizing Networks 272.2.2 Self-Configuration 282.2.3 Self-Optimization 282.2.4 Self-Healing 282.2.5 Key Performance Indicators 292.2.6 SON Functions 292.3 Cognitive Networks 292.4 Introduction to Machine Learning 302.4.1 ML Types 312.4.2 Components of ML Algorithms 312.4.3 How do Machines Learn? 322.4.3.1 Supervised Learning 322.4.3.2 Unsupervised Learning 332.4.3.3 Semi-Supervised Learning 352.4.3.4 Reinforcement Learning 352.4.4 ML and Wireless Networks 362.5 Software-Defined Networks 362.5.1 SDN Architecture 372.5.2 The OpenFlow Protocol 382.5.3 SDN and ML 392.6 Cognitive Radio Networks 392.6.1 Sensing Methods 412.7 ML for Wireless Networks: Challenges and Solution Approaches 412.7.1 Cellular Networks 422.7.1.1 Energy Saving 422.7.1.2 Channel Access and Assignment 422.7.1.3 User Association and Load Balancing 432.7.1.4 Traffic Engineering 442.7.1.5 QoS/QoE Prediction 452.7.1.6 Security 452.7.2 Wireless Local Area Networks 462.7.2.1 Access Point Selection 472.7.2.2 Interference Mitigation 482.7.2.3 Channel Allocation and Channel Bonding 492.7.2.4 Latency Estimation and Frame Length Selection 492.7.2.5 Handover 492.7.3 Cognitive Radio Networks 50References 503 An Overview on Internet of Things (IoT) Segments and Technologies 57Amarjit Singh3.1 Introduction 573.2 Features of IoT 593.3 IoT Sensor Devices 593.4 IoT Architecture 613.5 Challenges and Issues in IoT 623.6 Future Opportunities in IoT 633.7 Discussion 643.8 Conclusion 65References 654 The Technological Shift: AI in Big Data and IoT 69Deepti Sharma, Amandeep Singh and Sanyam Singhal4.1 Introduction 694.2 Artificial Intelligence 714.2.1 Machine Learning 714.2.2 Further Development in the Domain of Artificial Intelligence 73i4.2.3 Programming Languages for Artificial Intelligence 744.2.4 Outcomes of Artificial Intelligence 744.3 Big Data 754.3.1 Artificial Intelligence Methods for Big Data 774.3.2 Industry Perspective of Big Data 774.3.2.1 In Medical Field 784.3.2.2 In Meteorological Department 784.3.2.3 In Industrial/Corporate Applications and Analytics 794.3.2.4 In Education 794.3.2.5 In Astronomy 794.4 Internet of Things 804.4.1 Interconnection of IoT With AoT 814.4.2 Difference Between IIoT and IoT 814.4.3 Industrial Approach for IoT 824.5 Technical Shift in AI, Big Data, and IoT 824.5.1 Industries Shifting to AI-Enabled Big Data Analytics 834.5.2 Industries Shifting to AI-Powered IoT Devices 844.5.3 Statistical Data of These Shifts 844.6 Conclusion 85References 865 IoT’s Data Processing Using Spark 91Ankita Bansal and Aditya Atri5.1 Introduction 915.2 Introduction to Apache Spark 925.2.1 Advantages of Apache Spark 935.2.2 Apache Spark’s Components 935.3 Apache Hadoop MapReduce 945.3.1 Limitations of MapReduce 945.4 Resilient Distributed Dataset (RDD) 955.4.1 Features and Limitations of RDDs 955.5 DataFrames 965.6 Datasets 975.7 Introduction to Spark SQL 985.7.1 Spark SQL Architecture 995.7.2 Spark SQL Libraries 1005.8 SQL Context Class in Spark 1005.9 Creating Dataframes 1015.9.1 Operations on DataFrames 1025.10 Aggregations 1035.11 Running SQL Queries on Dataframes 1035.12 Integration With RDDs 1045.12.1 Inferring the Schema Using Reflection 1045.12.2 Specifying the Schema Programmatically 1045.13 Data Sources 1045.13.1 JSON Datasets 1055.13.2 Hive Tables 1055.13.3 Parquet Files 1065.14 Operations on Data Sources 1065.15 Industrial Applications 1075.16 Conclusion 108References 1086 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs 111Tayyab Khan and Karan Singh6.1 Introduction 1116.1.1 Components of WSNs 1136.1.2 Trust 1156.1.3 Major Contribution 1206.2 Related Work 1216.3 Network Topology and Assumptions 1226.4 Proposed Trust Model 1226.4.1 CM to CM (Direct) Trust Evaluation Scheme 1236.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx,y(∆t)) 1246.4.3 CH-to-CH Direct Trust Estimation 1256.4.4 BS-to-CH Feedback Trust Calculation 1256.5 Result and Analysis 1266.5.1 Severity Analysis 1266.5.2 Malicious Node Detection 1276.6 Conclusion and Future Work 128References 1287 Smart Applications of IoT 131Pradeep Kamboj, T. Ratha Jeyalakshmi, P. Thillai Arasu, S. Balamurali and A. Murugan7.1 Introduction 1317.2 Background 1327.2.1 Enabling Technologies for Building Intelligent Infrastructure 1327.3 Smart City 1367.3.1 Benefits of a Smart City 1377.3.2 Smart City Ecosystem 1377.3.3 Challenges in Smart Cities 1387.4 Smart Healthcare 1397.4.1 Smart Healthcare Applications 1407.4.2 Challenges in Healthcare 1417.5 Smart Agriculture 1427.5.1 Environment Agriculture Controlling 1437.5.2 Advantages 1437.5.3 Challenges 1447.6 Smart Industries 1457.6.1 Advantages 1477.6.2 Challenges 1487.7 Future Research Directions 1497.8 Conclusions 149References 1498 Sensor-Based Irrigation System: Introducing Technology in Agriculture 153Rohit Rastogi, Krishna Vir Singh, Mihir Rai, Kartik Sachdeva, Tarun Yadav and Harshit Gupta8.1 Introduction 1538.1.1 Technology in Agriculture 1548.1.2 Use and Need for Low-Cost Technology in Agriculture 1548.2 Proposed System 1548.3 Flow Chart 1578.4 Use Case 1588.5 System Modules 1588.5.1 Raspberry Pi 1588.5.2 Arduino Uno 1588.5.3 DHT 11 Humidity and Temperature Sensor 1588.5.4 Soil Moisture Sensor 1608.5.5 Solenoid Valve 1608.5.6 Drip Irrigation Kit 1608.5.7 433 MHz RF Module 1608.5.8 Mobile Application 1608.5.9 Testing Phase 1618.6 Limitations 1628.7 Suggestions 1628.8 Future Scope 1628.9 Conclusion 163Acknowledgement 163References 163Suggested Additional Readings 164Key Terms and Definitions 164Appendix 165Example Code 1669 Artificial Intelligence: An Imaginary World of Machine 167Bharat C. Patel, Manish M. Kaysth and Tejaskumar R. Ghadiyali9.1 The Dawn of Artificial Intelligence 1679.2 Introduction 1699.3 Components of AI 1709.3.1 Machine Reasoning 1709.3.2 Natural Language Processing 1719.3.3 Automated Planning 1719.3.4 Machine Learning 1719.4 Types of Artificial Intelligence 1729.4.1 Artificial Narrow Intelligence 1729.4.2 Artificial General Intelligence 1739.4.3 Artificial Super Intelligence 1749.5 Application Area of AI 1759.6 Challenges in Artificial Intelligence 1769.7 Future Trends in Artificial Intelligence 1779.8 Practical Implementation of AI Application 179References 18210 Impact of Deep Learning Techniques in IoT 185M. Chandra Vadhana, P. Shanthi Bala and Immanuel Zion Ramdinthara10.1 Introduction 18510.2 Internet of Things 18610.2.1 Characteristics of IoT 18710.2.2 Architecture of IoT 18710.2.2.1 Smart Device/Sensor Layer 18710.2.2.2 Gateways and Networks 18710.2.2.3 Management Service Layer 18810.2.2.4 Application Layer 18810.2.2.5 Interoperability of IoT 18810.2.2.6 Security Requirements at a Different Layer of IoT 19010.2.2.7 Future Challenges for IoT 19010.2.2.8 Privacy and Security 19010.2.2.9 Cost and Usability 19110.2.2.10 Data Management 19110.2.2.11 Energy Preservation 19110.2.2.12 Applications of IoT 19110.2.2.13 Essential IoT Technologies 19310.2.2.14 Enriching the Customer Value 19510.2.2.15 Evolution of the Foundational IoT Technologies 19610.2.2.16 Technical Challenges in the IoT Environment 19610.2.2.17 Security Challenge 19710.2.2.18 Chaos Challenge 19710.2.2.19 Advantages of IoT 19810.2.2.20 Disadvantages of IoT 19810.3 Deep Learning 19810.3.1 Models of Deep Learning 19910.3.1.1 Convolutional Neural Network 19910.3.1.2 Recurrent Neural Networks 19910.3.1.3 Long Short-Term Memory 20010.3.1.4 Autoencoders 20010.3.1.5 Variational Autoencoders 20110.3.1.6 Generative Adversarial Networks 20110.3.1.7 Restricted Boltzmann Machine 20110.3.1.8 Deep Belief Network 20110.3.1.9 Ladder Networks 20210.3.2 Applications of Deep Learning 20210.3.2.1 Industrial Robotics 20210.3.2.2 E-Commerce Industries 20210.3.2.3 Self-Driving Cars 20210.3.2.4 Voice-Activated Assistants 20210.3.2.5 Automatic Machine Translation 20210.3.2.6 Automatic Handwriting Translation 20310.3.2.7 Predicting Earthquakes 20310.3.2.8 Object Classification in Photographs 20310.3.2.9 Automatic Game Playing 20310.3.2.10 Adding Sound to Silent Movies 20310.3.3 Advantages of Deep Learning 20310.3.4 Disadvantages of Deep Learning 20310.3.5 Deployment of Deep Learning in IoT 20310.3.6 Deep Learning Applications in IoT 20410.3.6.1 Image Recognition 20410.3.6.2 Speech/Voice Recognition 20410.3.6.3 Indoor Localization 20410.3.6.4 Physiological and Psychological Detection 20510.3.6.5 Security and Privacy 20510.3.7 Deep Learning Techniques on IoT Devices 20510.3.7.1 Network Compression 20510.3.7.2 Approximate Computing 20610.3.7.3 Accelerators 20610.3.7.4 Tiny Motes 20610.4 IoT Challenges on Deep Learning and Future Directions 20610.4.1 Lack of IoT Dataset 20610.4.2 Pre-Processing 20710.4.3 Challenges of 6V’s 20710.4.4 Deep Learning Limitations 20710.5 Future Directions of Deep Learning 20710.5.1 IoT Mobile Data 20710.5.2 Integrating Contextual Information 20810.5.3 Online Resource Provisioning for IoT Analytics 20810.5.4 Semi-Supervised Analytic Framework 20810.5.5 Dependable and Reliable IoT Analytics 20810.5.6 Self-Organizing Communication Networks 20810.5.7 Emerging IoT Applications 20810.5.7.1 Unmanned Aerial Vehicles 20910.5.7.2 Virtual/Augmented Reality 20910.5.7.3 Mobile Robotics 20910.6 Common Datasets for Deep Learning in IoT 20910.7 Discussion 20910.8 Conclusion 211References 211Part 2: Artificial Intelligence in Healthcare 21511 Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques 217Toufique A. Soomro, Ahmed J. Afifi, Pardeep Kumar, Muhammad Usman Keerio, Saleem Ahmed and Ahmed Ali11.1 Introduction 21711.2 Existing Methods Review 22111.3 Methodology 22311.3.1 Architecture of Stride U-Net 22311.3.2 Loss Function 22511.4 Databases and Evaluation Metrics 22511.4.1 CNN Implementation Details 22611.5 Results and Analysis 22711.5.1 Evaluation on DRIVE and STARE Databases 22711.5.2 Comparative Analysis 22711.6 Concluding Remarks 229References 23012 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review 235Muhammad Ali Nizamani, Muhammad Ali Memon and Pirah Brohi12.1 Introduction 23512.2 Methodology 23712.3 IoT in Mental Health 23812.4 Mental Healthcare Applications and Services Based on IoT 23812.5 Benefits of IoT in Mental Health 24112.5.1 Reduction in Treatment Cost 24112.5.2 Reduce Human Error 24112.5.3 Remove Geographical Barriers 24112.5.4 Less Paperwork and Documentation 24112.5.5 Early Stage Detection of Chronic Disorders 24112.5.6 Improved Drug Management 24212.5.7 Speedy Medical Attention 24212.5.8 Reliable Results of Treatment 24212.6 Challenges in IoT-Based Mental Healthcare Applications 24212.6.1 Scalability 24212.6.2 Trust 24212.6.3 Security and Privacy Issues 24312.6.4 Interoperability Issues 24312.6.5 Computational Limits 24312.6.6 Memory Limitations 24312.6.7 Communications Media 24412.6.8 Devices Multiplicity 24412.6.9 Standardization 24412.6.10 IoT-Based Healthcare Platforms 24412.6.11 Network Type 24412.6.12 Quality of Service 24512.7 Blockchain in IoT for Healthcare 24512.8 Results and Discussion 24612.9 Limitations of the Survey 24712.10 Conclusion 247References 24713 Monitoring Technologies for Precision Health 251Rehab A. Rayan and Imran Zafar13.1 Introduction 25113.2 Applications of Monitoring Technologies 25213.2.1 Everyday Life Activities 25313.2.2 Sleeping and Stress 25313.2.3 Breathing Patterns and Respiration 25413.2.4 Energy and Caloric Consumption 25413.2.5 Diabetes, Cardiac, and Cognitive Care 25413.2.6 Disability and Rehabilitation 25413.2.7 Pregnancy and Post-Procedural Care 25513.3 Limitations 25513.3.1 Quality of Data and Reliability 25513.3.2 Safety, Privacy, and Legal Concerns 25613.4 Future Insights 25613.4.1 Consolidating Frameworks 25613.4.2 Monitoring and Intervention 25613.4.3 Research and Development 25713.5 Conclusions 257References 25714 Impact of Artificial Intelligence in Cardiovascular Disease 261Mir Khan, Saleem Ahmed, Pardeep Kumar and Dost Muhammad Saqib Bhatti14.1 Artificial Intelligence 26114.2 Machine Learning 26214.3 The Application of AI in CVD 26314.3.1 Precision Medicine 26314.3.2 Clinical Prediction 26314.3.3 Cardiac Imaging Analysis 26414.4 Future Prospect 26414.5 PUAI and Novel Medical Mode 26514.5.1 Phenomenon of PUAI 26514.5.2 Novel Medical Model 26614.6 Traditional Mode 26614.6.1 Novel Medical Mode Plus PUAI 26614.7 Representative Calculations of AI 26814.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis 268References 27015 Healthcare Transformation With Clinical Big Data Predictive Analytics 273Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, Mumtaz Qabulio, Sumera Naz Pathan and Asia Khatoon Soomro15.1 Introduction 27315.1.1 Big Data in Health Sector 27515.1.2 Data Structure Produced in Health Sectors 27515.2 Big Data Challenges in Healthcare 27615.2.1 Big Data in Computational Healthcare 27615.2.2 Big Data Predictive Analytics in Healthcare 27615.2.3 Big Data for Adapted Healthcare 27715.3 Cloud Computing and Big Data in Healthcare 27815.4 Big Data Healthcare and IoT 27815.5 Wearable Devices for Patient Health Monitoring 28215.6 Big Data and Industry 4.0 28315.7 Conclusion 283References 28416 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats 287Rohit Rastogi, Mamta Saxena, D.K. Chaturvedi, Mayank Gupta, Mukund Rastogi, Prajwal Srivatava, Mohit Jain, Pradeep Kumar, Ujjawal Sharma, Rohan Choudhary and Neha Gupta16.1 Introduction 28716.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate 28716.1.2 Precautionary Guidelines Followed in Indian Continent 28816.1.3 Spiritual Guidelines in Indian Society 28916.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India 28916.1.4 Veda Vigyaan: Ancient Vedic Knowledge 28916.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon 28916.1.6 The Yagya Samagri 29016.2 Literature Survey 29016.2.1 Technical Aspects of Yajna and Mantra Therapy 29016.2.2 Mantra Chanting and Its Science 29016.2.3 Yagya Medicine (Yagyopathy) 29016.2.4 The Medicinal HavanSamagri Components 29116.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases 29116.2.5 Scientific Benefits of Havan 29116.3 Experimental Setup Protocols With Results 29216.3.1 Subject Sample Distribution 29516.3.1.1 Area Wise Distribution 29516.3.2 Conclusion and Discussion Through Experimental Work 29516.4 Future Scope and Limitations 29716.5 Novelty 29816.6 Recommendations 29816.7 Applications of Yajna Therapy 29916.8 Conclusions 299Acknowledgement 299References 299Key Terms and Definitions 30417 Extraction of Depression Symptoms From Social Networks 307Bhavna Chilwal and Amit Kumar Mishra17.1 Introduction 30717.1.1 Diagnosis and Treatments 30917.2 Data Mining in Healthcare 31017.2.1 Text Mining 31017.3 Social Network Sites 31117.4 Symptom Extraction Tool 31217.4.1 Data Collection 31317.4.2 Data Processing 31317.4.3 Data Analysis 31417.5 Sentiment Analysis 31617.5.1 Emotion Analysis 31817.5.2 Behavioral Analysis 31817.6 Conclusion 319References 320Part 3: Cybersecurity 32318 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations 325C. Kaviyazhiny, P. Shanthi Bala and A.S. Gowri18.1 Introduction 32518.2 Characteristics of Fog Computing 32618.3 Reference Architecture of Fog Computing 32818.4 CISCO IOx Framework 32918.5 Security Practices in CISCO IOx 33018.5.1 Potential Attacks on IoT Architecture 33018.5.2 Perception Layer (Sensing) 33118.5.3 Network Layer 33118.5.4 Service Layer (Support) 33218.5.5 Application Layer (Interface) 33318.6 Security Issues in Fog Computing 33318.6.1 Virtualization Issues 33318.6.2 Web Security Issues 33418.6.3 Internal/External Communication Issues 33518.6.4 Data Security Related Issues 33618.6.5 Wireless Security Issues 33718.6.6 Malware Protection 33818.7 Machine Learning for Secure Fog Computing 33818.7.1 Layer 1 Cloud 33918.7.2 Layer 2 Fog Nodes For The Community 34018.7.3 Layer 3 Fog Node for Their Neighborhood 34018.7.4 Layer 4 Sensors 34118.8 Existing Security Solution in Fog Computing 34118.8.1 Privacy-Preserving in Fog Computing 34118.8.2 Pseudocode for Privacy Preserving in Fog Computing 34218.8.3 Pseudocode for Feature Extraction 34318.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature 34318.8.5 Pseudocode for Encrypting Data 34418.8.6 Pseudocode for Data Partitioning 34418.8.7 Encryption Algorithms in Fog Computing 34518.9 Recommendation and Future Enhancement 34518.9.1 Data Encryption 34518.9.2 Preventing from Cache Attacks 34618.9.3 Network Monitoring 34618.9.4 Malware Protection 34718.9.5 Wireless Security 34718.9.6 Secured Vehicular Network 34718.9.7 Secure Multi-Tenancy 34818.9.8 Backup and Recovery 34818.9.9 Security with Performance 34818.10 Conclusion 349References 34919 Cybersecurity and Privacy Fundamentals 353Ravi Verma19.1 Introduction 35319.2 Historical Background and Evolution of Cyber Crime 35419.3 Introduction to Cybersecurity 35519.3.1 Application Security 35619.3.2 Information Security 35619.3.3 Recovery From Failure or Disaster 35619.3.4 Network Security 35719.4 Classification of Cyber Crimes 35719.4.1 Internal Attacks 35719.4.2 External Attacks 35819.4.3 Unstructured Attack 35819.4.4 Structured Attack 35819.5 Reasons Behind Cyber Crime 35819.5.1 Making Money 35919.5.2 Gaining Financial Growth and Reputation 35919.5.3 Revenge 35919.5.4 For Making Fun 35919.5.5 To Recognize 35919.5.6 Business Analysis and Decision Making 35919.6 Various Types of Cyber Crime 35919.6.1 Cyber Stalking 36019.6.2 Sexual Harassment or Child Pornography 36019.6.3 Forgery 36019.6.4 Crime Related to Privacy of Software and Network Resources 36019.6.5 Cyber Terrorism 36019.6.6 Phishing, Vishing, and Smishing 36019.6.7 Malfunction 36119.6.8 Server Hacking 36119.6.9 Spreading Virus 36119.6.10 Spamming, Cross Site Scripting, and Web Jacking 36119.7 Various Types of Cyber Attacks in Information Security 36119.7.1 Web-Based Attacks in Information Security 36219.7.2 System-Based Attacks in Information Security 36419.8 Cybersecurity and Privacy Techniques 36519.8.1 Authentication and Authorization 36519.8.2 Cryptography 36619.8.2.1 Symmetric Key Encryption 36719.8.2.2 Asymmetric Key Encryption 36719.8.3 Installation of Antivirus 36719.8.4 Digital Signature 36719.8.5 Firewall 36919.8.6 Steganography 36919.9 Essential Elements of Cybersecurity 37019.10 Basic Security Concerns for Cybersecurity 37119.10.1 Precaution 37219.10.2 Maintenance 37219.10.3 Reactions 37319.11 Cybersecurity Layered Stack 37319.12 Basic Security and Privacy Check List 37419.13 Future Challenges of Cybersecurity 374References 37620 Changing the Conventional Banking System through Blockchain 379Khushboo Tripathi, Neha Bhateja and Ashish Dhillon20.1 Introduction 37920.1.1 Introduction to Blockchain 37920.1.2 Classification of Blockchains 38120.1.2.1 Public Blockchain 38120.1.2.2 Private Blockchain 38220.1.2.3 Hybrid Blockchain 38220.1.2.4 Consortium Blockchain 38220.1.3 Need for Blockchain Technology 38320.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary 38320.1.4 Comparison of Blockchain and Cryptocurrency 38420.1.4.1 Distributed Ledger Technology (DLT) 38420.1.5 Types of Consensus Mechanism 38520.1.5.1 Consensus Algorithm: A Quick Background 38520.1.6 Proof of Work 38620.1.7 Proof of Stake 38720.1.7.1 Delegated Proof of Stake 38720.1.7.2 Byzantine Fault Tolerance 38820.2 Literature Survey 38820.2.1 The History of Blockchain Technology 38820.2.2 Early Years of Blockchain Technology: 1991–2008 38920.2.2.1 Evolution of Blockchain: Phase 1—Transactions 38920.2.2.2 Evolution of Blockchain: Phase 2—Contracts 39020.2.2.3 Evolution of Blockchain: Phase 3—Applications 39020.2.3 Literature Review 39120.2.4 Analysis 39220.3 Methodology and Tools 39220.3.1 Methodology 39220.3.2 Flow Chart 39320.3.3 Tools and Configuration 39420.4 Experiment 39420.4.1 Steps of Implementation 39420.4.2 Screenshots of Experiment 39720.5 Results 39820.6 Conclusion 40020.7 Future Scope 40120.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises 401References 40221 A Secured Online Voting System by Using Blockchain as the Medium 405Leslie Mark, Vasaki Ponnusamy, Arya Wicaksana, Basilius Bias Christyono and Moeljono Widjaja21.1 Blockchain-Based Online Voting System 40521.1.1 Introduction 40521.1.2 Structure of a Block in a Blockchain System 40621.1.3 Function of Segments in a Block of the Blockchain 40621.1.4 SHA-256 Hashing on the Blockchain 40721.1.5 Interaction Involved in Blockchain-Based Online Voting System 40921.1.6 Online Voting System Using Blockchain – Framework 40921.2 Literature Review 41021.2.1 Literature Review Outline 41021.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model 41021.2.1.2 Online Voting System Based on Visual Cryptography 41121.2.1.3 Online Voting System Using Biometric Security and Steganography 41221.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption 41421.2.1.5 An Online Voting System Based on a Secured Blockchain 41621.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach 41721.2.1.7 Online Voting System Using Iris Recognition 41821.2.1.8 Online Voting System Based on NID and SIM 42021.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography 42221.2.1.10 Online Voting System Using Secret Sharing–Based Authentication 42521.2.2 Comparing the Existing Online Voting System 427References 43022 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects 431Abhinav Juneja, Sapna Juneja, Vikram Bali, Vishal Jain and Hemant Upadhyay22.1 Introduction 43122.2 Literature Review 43222.3 Different Variants of Cybersecurity in Action 43222.4 Importance of Cybersecurity in Action 43322.5 Methods for Establishing a Strategy for Cybersecurity 43422.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity 43422.7 Where AI Is Actually Required to Deal With Cybersecurity 43722.8 Challenges for Cybersecurity in Current State of Practice 43822.9 Conclusion 438References 438Index 443