The Smart Cyber Ecosystem for Sustainable Development
1. Auflage November 2021
480 Seiten, Hardcover
Wiley & Sons Ltd
ISBN:
978-1-119-76164-8
John Wiley & Sons
Weitere Versionen
Das Cyber-Ökosystem kann unser natürliches Ökosystem nachbilden, in dem verschiedene lebende und nicht lebende Dinge miteinander interagieren, um bestimmte Aufgaben zu erfüllen. Ganz ähnlich arbeiten die verschiedenen Einheiten des Cyber-Ökosystems auf digitaler Ebene zusammen, um unseren Lebensstil durch die Schaffung intelligenter und automatisierter Systeme/Prozesse zu revolutionieren. Die Hauptakteure des Cyber-Ökosystems sind insbesondere das Internet der Dinge (IoT), die künstliche Intelligenz (KI) und Mechanismen zur Gewährleistung der Cybersicherheit.
In diesem Buch wird aufgezeigt, wie die Kombination dieser Technologien eine digitale, nachhaltige sozioökonomische Infrastruktur ermöglicht, die unsere Lebensqualität verbessert. Es werden fortschrittliche Automatisierungsmethoden vorgestellt, die mit verbesserten Geschäfts- und Prüfungsmodellen, universellen Authentifizierungsschemata, transparenter Governance und innovativen Prognosen einhergehen.
Preface xxi
Part 1: Internet of Things 1
1 Voyage of Internet of Things in the Ocean of Technology 3
Tejaskumar R. Ghadiyali, Bharat C. Patel and Manish M. Kayasth
1.1 Introduction 3
1.1.1 Characteristics of IoT 4
1.1.2 IoT Architecture 5
1.1.3 Merits and Demerits of IoT 6
1.2 Technological Evolution Toward IoT 7
1.3 IoT-Associated Technology 8
1.4 Interoperability in IoT 14
1.5 Programming Technologies in IoT 15
1.5.1 Arduino 15
1.5.2 Raspberry Pi 17
1.5.3 Python 18
1.6 IoT Applications 19
Conclusion 22
References 22
2 AI for Wireless Network Optimization: Challenges and Opportunities 25
Murad Abusubaih
2.1 Introduction to AI 25
2.2 Self-Organizing Networks 27
2.2.1 Operation Principle of Self-Organizing Networks 27
2.2.2 Self-Configuration 28
2.2.3 Self-Optimization 28
2.2.4 Self-Healing 28
2.2.5 Key Performance Indicators 29
2.2.6 SON Functions 29
2.3 Cognitive Networks 29
2.4 Introduction to Machine Learning 30
2.4.1 ML Types 31
2.4.2 Components of ML Algorithms 31
2.4.3 How do Machines Learn? 32
2.4.3.1 Supervised Learning 32
2.4.3.2 Unsupervised Learning 33
2.4.3.3 Semi-Supervised Learning 35
2.4.3.4 Reinforcement Learning 35
2.4.4 ML and Wireless Networks 36
2.5 Software-Defined Networks 36
2.5.1 SDN Architecture 37
2.5.2 The OpenFlow Protocol 38
2.5.3 SDN and ML 39
2.6 Cognitive Radio Networks 39
2.6.1 Sensing Methods 41
2.7 ML for Wireless Networks: Challenges and Solution Approaches 41
2.7.1 Cellular Networks 42
2.7.1.1 Energy Saving 42
2.7.1.2 Channel Access and Assignment 42
2.7.1.3 User Association and Load Balancing 43
2.7.1.4 Traffic Engineering 44
2.7.1.5 QoS/QoE Prediction 45
2.7.1.6 Security 45
2.7.2 Wireless Local Area Networks 46
2.7.2.1 Access Point Selection 47
2.7.2.2 Interference Mitigation 48
2.7.2.3 Channel Allocation and Channel Bonding 49
2.7.2.4 Latency Estimation and Frame Length Selection 49
2.7.2.5 Handover 49
2.7.3 Cognitive Radio Networks 50
References 50
3 An Overview on Internet of Things (IoT) Segments and Technologies 57
Amarjit Singh
3.1 Introduction 57
3.2 Features of IoT 59
3.3 IoT Sensor Devices 59
3.4 IoT Architecture 61
3.5 Challenges and Issues in IoT 62
3.6 Future Opportunities in IoT 63
3.7 Discussion 64
3.8 Conclusion 65
References 65
4 The Technological Shift: AI in Big Data and IoT 69
Deepti Sharma, Amandeep Singh and Sanyam Singhal
4.1 Introduction 69
4.2 Artificial Intelligence 71
4.2.1 Machine Learning 71
4.2.2 Further Development in the Domain of Artificial Intelligence 73i
4.2.3 Programming Languages for Artificial Intelligence 74
4.2.4 Outcomes of Artificial Intelligence 74
4.3 Big Data 75
4.3.1 Artificial Intelligence Methods for Big Data 77
4.3.2 Industry Perspective of Big Data 77
4.3.2.1 In Medical Field 78
4.3.2.2 In Meteorological Department 78
4.3.2.3 In Industrial/Corporate Applications and Analytics 79
4.3.2.4 In Education 79
4.3.2.5 In Astronomy 79
4.4 Internet of Things 80
4.4.1 Interconnection of IoT With AoT 81
4.4.2 Difference Between IIoT and IoT 81
4.4.3 Industrial Approach for IoT 82
4.5 Technical Shift in AI, Big Data, and IoT 82
4.5.1 Industries Shifting to AI-Enabled Big Data Analytics 83
4.5.2 Industries Shifting to AI-Powered IoT Devices 84
4.5.3 Statistical Data of These Shifts 84
4.6 Conclusion 85
References 86
5 IoT's Data Processing Using Spark 91
Ankita Bansal and Aditya Atri
5.1 Introduction 91
5.2 Introduction to Apache Spark 92
5.2.1 Advantages of Apache Spark 93
5.2.2 Apache Spark's Components 93
5.3 Apache Hadoop MapReduce 94
5.3.1 Limitations of MapReduce 94
5.4 Resilient Distributed Dataset (RDD) 95
5.4.1 Features and Limitations of RDDs 95
5.5 DataFrames 96
5.6 Datasets 97
5.7 Introduction to Spark SQL 98
5.7.1 Spark SQL Architecture 99
5.7.2 Spark SQL Libraries 100
5.8 SQL Context Class in Spark 100
5.9 Creating Dataframes 101
5.9.1 Operations on DataFrames 102
5.10 Aggregations 103
5.11 Running SQL Queries on Dataframes 103
5.12 Integration With RDDs 104
5.12.1 Inferring the Schema Using Reflection 104
5.12.2 Specifying the Schema Programmatically 104
5.13 Data Sources 104
5.13.1 JSON Datasets 105
5.13.2 Hive Tables 105
5.13.3 Parquet Files 106
5.14 Operations on Data Sources 106
5.15 Industrial Applications 107
5.16 Conclusion 108
References 108
6 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs 111
Tayyab Khan and Karan Singh
6.1 Introduction 111
6.1.1 Components of WSNs 113
6.1.2 Trust 115
6.1.3 Major Contribution 120
6.2 Related Work 121
6.3 Network Topology and Assumptions 122
6.4 Proposed Trust Model 122
6.4.1 CM to CM (Direct) Trust Evaluation Scheme 123
6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx,y(deltat)) 124
6.4.3 CH-to-CH Direct Trust Estimation 125
6.4.4 BS-to-CH Feedback Trust Calculation 125
6.5 Result and Analysis 126
6.5.1 Severity Analysis 126
6.5.2 Malicious Node Detection 127
6.6 Conclusion and Future Work 128
References 128
7 Smart Applications of IoT 131
Pradeep Kamboj, T. Ratha Jeyalakshmi, P. Thillai Arasu, S. Balamurali and A. Murugan
7.1 Introduction 131
7.2 Background 132
7.2.1 Enabling Technologies for Building Intelligent Infrastructure 132
7.3 Smart City 136
7.3.1 Benefits of a Smart City 137
7.3.2 Smart City Ecosystem 137
7.3.3 Challenges in Smart Cities 138
7.4 Smart Healthcare 139
7.4.1 Smart Healthcare Applications 140
7.4.2 Challenges in Healthcare 141
7.5 Smart Agriculture 142
7.5.1 Environment Agriculture Controlling 143
7.5.2 Advantages 143
7.5.3 Challenges 144
7.6 Smart Industries 145
7.6.1 Advantages 147
7.6.2 Challenges 148
7.7 Future Research Directions 149
7.8 Conclusions 149
References 149
8 Sensor-Based Irrigation System: Introducing Technology in Agriculture 153
Rohit Rastogi, Krishna Vir Singh, Mihir Rai, Kartik Sachdeva, Tarun Yadav and Harshit Gupta
8.1 Introduction 153
8.1.1 Technology in Agriculture 154
8.1.2 Use and Need for Low-Cost Technology in Agriculture 154
8.2 Proposed System 154
8.3 Flow Chart 157
8.4 Use Case 158
8.5 System Modules 158
8.5.1 Raspberry Pi 158
8.5.2 Arduino Uno 158
8.5.3 DHT 11 Humidity and Temperature Sensor 158
8.5.4 Soil Moisture Sensor 160
8.5.5 Solenoid Valve 160
8.5.6 Drip Irrigation Kit 160
8.5.7 433 MHz RF Module 160
8.5.8 Mobile Application 160
8.5.9 Testing Phase 161
8.6 Limitations 162
8.7 Suggestions 162
8.8 Future Scope 162
8.9 Conclusion 163
Acknowledgement 163
References 163
Suggested Additional Readings 164
Key Terms and Definitions 164
Appendix 165
Example Code 166
9 Artificial Intelligence: An Imaginary World of Machine 167
Bharat C. Patel, Manish M. Kaysth and Tejaskumar R. Ghadiyali
9.1 The Dawn of Artificial Intelligence 167
9.2 Introduction 169
9.3 Components of AI 170
9.3.1 Machine Reasoning 170
9.3.2 Natural Language Processing 171
9.3.3 Automated Planning 171
9.3.4 Machine Learning 171
9.4 Types of Artificial Intelligence 172
9.4.1 Artificial Narrow Intelligence 172
9.4.2 Artificial General Intelligence 173
9.4.3 Artificial Super Intelligence 174
9.5 Application Area of AI 175
9.6 Challenges in Artificial Intelligence 176
9.7 Future Trends in Artificial Intelligence 177
9.8 Practical Implementation of AI Application 179
References 182
10 Impact of Deep Learning Techniques in IoT 185
M. Chandra Vadhana, P. Shanthi Bala and Immanuel Zion Ramdinthara
10.1 Introduction 185
10.2 Internet of Things 186
10.2.1 Characteristics of IoT 187
10.2.2 Architecture of IoT 187
10.2.2.1 Smart Device/Sensor Layer 187
10.2.2.2 Gateways and Networks 187
10.2.2.3 Management Service Layer 188
10.2.2.4 Application Layer 188
10.2.2.5 Interoperability of IoT 188
10.2.2.6 Security Requirements at a Different Layer of IoT 190
10.2.2.7 Future Challenges for IoT 190
10.2.2.8 Privacy and Security 190
10.2.2.9 Cost and Usability 191
10.2.2.10 Data Management 191
10.2.2.11 Energy Preservation 191
10.2.2.12 Applications of IoT 191
10.2.2.13 Essential IoT Technologies 193
10.2.2.14 Enriching the Customer Value 195
10.2.2.15 Evolution of the Foundational IoT Technologies 196
10.2.2.16 Technical Challenges in the IoT Environment 196
10.2.2.17 Security Challenge 197
10.2.2.18 Chaos Challenge 197
10.2.2.19 Advantages of IoT 198
10.2.2.20 Disadvantages of IoT 198
10.3 Deep Learning 198
10.3.1 Models of Deep Learning 199
10.3.1.1 Convolutional Neural Network 199
10.3.1.2 Recurrent Neural Networks 199
10.3.1.3 Long Short-Term Memory 200
10.3.1.4 Autoencoders 200
10.3.1.5 Variational Autoencoders 201
10.3.1.6 Generative Adversarial Networks 201
10.3.1.7 Restricted Boltzmann Machine 201
10.3.1.8 Deep Belief Network 201
10.3.1.9 Ladder Networks 202
10.3.2 Applications of Deep Learning 202
10.3.2.1 Industrial Robotics 202
10.3.2.2 E-Commerce Industries 202
10.3.2.3 Self-Driving Cars 202
10.3.2.4 Voice-Activated Assistants 202
10.3.2.5 Automatic Machine Translation 202
10.3.2.6 Automatic Handwriting Translation 203
10.3.2.7 Predicting Earthquakes 203
10.3.2.8 Object Classification in Photographs 203
10.3.2.9 Automatic Game Playing 203
10.3.2.10 Adding Sound to Silent Movies 203
10.3.3 Advantages of Deep Learning 203
10.3.4 Disadvantages of Deep Learning 203
10.3.5 Deployment of Deep Learning in IoT 203
10.3.6 Deep Learning Applications in IoT 204
10.3.6.1 Image Recognition 204
10.3.6.2 Speech/Voice Recognition 204
10.3.6.3 Indoor Localization 204
10.3.6.4 Physiological and Psychological Detection 205
10.3.6.5 Security and Privacy 205
10.3.7 Deep Learning Techniques on IoT Devices 205
10.3.7.1 Network Compression 205
10.3.7.2 Approximate Computing 206
10.3.7.3 Accelerators 206
10.3.7.4 Tiny Motes 206
10.4 IoT Challenges on Deep Learning and Future Directions 206
10.4.1 Lack of IoT Dataset 206
10.4.2 Pre-Processing 207
10.4.3 Challenges of 6V's 207
10.4.4 Deep Learning Limitations 207
10.5 Future Directions of Deep Learning 207
10.5.1 IoT Mobile Data 207
10.5.2 Integrating Contextual Information 208
10.5.3 Online Resource Provisioning for IoT Analytics 208
10.5.4 Semi-Supervised Analytic Framework 208
10.5.5 Dependable and Reliable IoT Analytics 208
10.5.6 Self-Organizing Communication Networks 208
10.5.7 Emerging IoT Applications 208
10.5.7.1 Unmanned Aerial Vehicles 209
10.5.7.2 Virtual/Augmented Reality 209
10.5.7.3 Mobile Robotics 209
10.6 Common Datasets for Deep Learning in IoT 209
10.7 Discussion 209
10.8 Conclusion 211
References 211
Part 2: Artificial Intelligence in Healthcare 215
11 Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques 217
Toufique A. Soomro, Ahmed J. Afifi, Pardeep Kumar, Muhammad Usman Keerio, Saleem Ahmed and Ahmed Ali
11.1 Introduction 217
11.2 Existing Methods Review 221
11.3 Methodology 223
11.3.1 Architecture of Stride U-Net 223
11.3.2 Loss Function 225
11.4 Databases and Evaluation Metrics 225
11.4.1 CNN Implementation Details 226
11.5 Results and Analysis 227
11.5.1 Evaluation on DRIVE and STARE Databases 227
11.5.2 Comparative Analysis 227
11.6 Concluding Remarks 229
References 230
12 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review 235
Muhammad Ali Nizamani, Muhammad Ali Memon and Pirah Brohi
12.1 Introduction 235
12.2 Methodology 237
12.3 IoT in Mental Health 238
12.4 Mental Healthcare Applications and Services Based on IoT 238
12.5 Benefits of IoT in Mental Health 241
12.5.1 Reduction in Treatment Cost 241
12.5.2 Reduce Human Error 241
12.5.3 Remove Geographical Barriers 241
12.5.4 Less Paperwork and Documentation 241
12.5.5 Early Stage Detection of Chronic Disorders 241
12.5.6 Improved Drug Management 242
12.5.7 Speedy Medical Attention 242
12.5.8 Reliable Results of Treatment 242
12.6 Challenges in IoT-Based Mental Healthcare Applications 242
12.6.1 Scalability 242
12.6.2 Trust 242
12.6.3 Security and Privacy Issues 243
12.6.4 Interoperability Issues 243
12.6.5 Computational Limits 243
12.6.6 Memory Limitations 243
12.6.7 Communications Media 244
12.6.8 Devices Multiplicity 244
12.6.9 Standardization 244
12.6.10 IoT-Based Healthcare Platforms 244
12.6.11 Network Type 244
12.6.12 Quality of Service 245
12.7 Blockchain in IoT for Healthcare 245
12.8 Results and Discussion 246
12.9 Limitations of the Survey 247
12.10 Conclusion 247
References 247
13 Monitoring Technologies for Precision Health 251
Rehab A. Rayan and Imran Zafar
13.1 Introduction 251
13.2 Applications of Monitoring Technologies 252
13.2.1 Everyday Life Activities 253
13.2.2 Sleeping and Stress 253
13.2.3 Breathing Patterns and Respiration 254
13.2.4 Energy and Caloric Consumption 254
13.2.5 Diabetes, Cardiac, and Cognitive Care 254
13.2.6 Disability and Rehabilitation 254
13.2.7 Pregnancy and Post-Procedural Care 255
13.3 Limitations 255
13.3.1 Quality of Data and Reliability 255
13.3.2 Safety, Privacy, and Legal Concerns 256
13.4 Future Insights 256
13.4.1 Consolidating Frameworks 256
13.4.2 Monitoring and Intervention 256
13.4.3 Research and Development 257
13.5 Conclusions 257
References 257
14 Impact of Artificial Intelligence in Cardiovascular Disease 261
Mir Khan, Saleem Ahmed, Pardeep Kumar and Dost Muhammad Saqib Bhatti
14.1 Artificial Intelligence 261
14.2 Machine Learning 262
14.3 The Application of AI in CVD 263
14.3.1 Precision Medicine 263
14.3.2 Clinical Prediction 263
14.3.3 Cardiac Imaging Analysis 264
14.4 Future Prospect 264
14.5 PUAI and Novel Medical Mode 265
14.5.1 Phenomenon of PUAI 265
14.5.2 Novel Medical Model 266
14.6 Traditional Mode 266
14.6.1 Novel Medical Mode Plus PUAI 266
14.7 Representative Calculations of AI 268
14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis 268
References 270
15 Healthcare Transformation With Clinical Big Data Predictive Analytics 273
Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, Mumtaz Qabulio, Sumera Naz Pathan and Asia Khatoon Soomro
15.1 Introduction 273
15.1.1 Big Data in Health Sector 275
15.1.2 Data Structure Produced in Health Sectors 275
15.2 Big Data Challenges in Healthcare 276
15.2.1 Big Data in Computational Healthcare 276
15.2.2 Big Data Predictive Analytics in Healthcare 276
15.2.3 Big Data for Adapted Healthcare 277
15.3 Cloud Computing and Big Data in Healthcare 278
15.4 Big Data Healthcare and IoT 278
15.5 Wearable Devices for Patient Health Monitoring 282
15.6 Big Data and Industry 4.0 283
15.7 Conclusion 283
References 284
16 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats 287
Rohit Rastogi, Mamta Saxena, D.K. Chaturvedi, Mayank Gupta, Mukund Rastogi, Prajwal Srivatava, Mohit Jain, Pradeep Kumar, Ujjawal Sharma, Rohan Choudhary and Neha Gupta
16.1 Introduction 287
16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate 287
16.1.2 Precautionary Guidelines Followed in Indian Continent 288
16.1.3 Spiritual Guidelines in Indian Society 289
16.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India 289
16.1.4 Veda Vigyaan: Ancient Vedic Knowledge 289
16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon 289
16.1.6 The Yagya Samagri 290
16.2 Literature Survey 290
16.2.1 Technical Aspects of Yajna and Mantra Therapy 290
16.2.2 Mantra Chanting and Its Science 290
16.2.3 Yagya Medicine (Yagyopathy) 290
16.2.4 The Medicinal HavanSamagri Components 291
16.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases 291
16.2.5 Scientific Benefits of Havan 291
16.3 Experimental Setup Protocols With Results 292
16.3.1 Subject Sample Distribution 295
16.3.1.1 Area Wise Distribution 295
16.3.2 Conclusion and Discussion Through Experimental Work 295
16.4 Future Scope and Limitations 297
16.5 Novelty 298
16.6 Recommendations 298
16.7 Applications of Yajna Therapy 299
16.8 Conclusions 299
Acknowledgement 299
References 299
Key Terms and Definitions 304
17 Extraction of Depression Symptoms From Social Networks 307
Bhavna Chilwal and Amit Kumar Mishra
17.1 Introduction 307
17.1.1 Diagnosis and Treatments 309
17.2 Data Mining in Healthcare 310
17.2.1 Text Mining 310
17.3 Social Network Sites 311
17.4 Symptom Extraction Tool 312
17.4.1 Data Collection 313
17.4.2 Data Processing 313
17.4.3 Data Analysis 314
17.5 Sentiment Analysis 316
17.5.1 Emotion Analysis 318
17.5.2 Behavioral Analysis 318
17.6 Conclusion 319
References 320
Part 3: Cybersecurity 323
18 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations 325
C. Kaviyazhiny, P. Shanthi Bala and A.S. Gowri
18.1 Introduction 325
18.2 Characteristics of Fog Computing 326
18.3 Reference Architecture of Fog Computing 328
18.4 CISCO IOx Framework 329
18.5 Security Practices in CISCO IOx 330
18.5.1 Potential Attacks on IoT Architecture 330
18.5.2 Perception Layer (Sensing) 331
18.5.3 Network Layer 331
18.5.4 Service Layer (Support) 332
18.5.5 Application Layer (Interface) 333
18.6 Security Issues in Fog Computing 333
18.6.1 Virtualization Issues 333
18.6.2 Web Security Issues 334
18.6.3 Internal/External Communication Issues 335
18.6.4 Data Security Related Issues 336
18.6.5 Wireless Security Issues 337
18.6.6 Malware Protection 338
18.7 Machine Learning for Secure Fog Computing 338
18.7.1 Layer 1 Cloud 339
18.7.2 Layer 2 Fog Nodes For The Community 340
18.7.3 Layer 3 Fog Node for Their Neighborhood 340
18.7.4 Layer 4 Sensors 341
18.8 Existing Security Solution in Fog Computing 341
18.8.1 Privacy-Preserving in Fog Computing 341
18.8.2 Pseudocode for Privacy Preserving in Fog Computing 342
18.8.3 Pseudocode for Feature Extraction 343
18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature 343
18.8.5 Pseudocode for Encrypting Data 344
18.8.6 Pseudocode for Data Partitioning 344
18.8.7 Encryption Algorithms in Fog Computing 345
18.9 Recommendation and Future Enhancement 345
18.9.1 Data Encryption 345
18.9.2 Preventing from Cache Attacks 346
18.9.3 Network Monitoring 346
18.9.4 Malware Protection 347
18.9.5 Wireless Security 347
18.9.6 Secured Vehicular Network 347
18.9.7 Secure Multi-Tenancy 348
18.9.8 Backup and Recovery 348
18.9.9 Security with Performance 348
18.10 Conclusion 349
References 349
19 Cybersecurity and Privacy Fundamentals 353
Ravi Verma
19.1 Introduction 353
19.2 Historical Background and Evolution of Cyber Crime 354
19.3 Introduction to Cybersecurity 355
19.3.1 Application Security 356
19.3.2 Information Security 356
19.3.3 Recovery From Failure or Disaster 356
19.3.4 Network Security 357
19.4 Classification of Cyber Crimes 357
19.4.1 Internal Attacks 357
19.4.2 External Attacks 358
19.4.3 Unstructured Attack 358
19.4.4 Structured Attack 358
19.5 Reasons Behind Cyber Crime 358
19.5.1 Making Money 359
19.5.2 Gaining Financial Growth and Reputation 359
19.5.3 Revenge 359
19.5.4 For Making Fun 359
19.5.5 To Recognize 359
19.5.6 Business Analysis and Decision Making 359
19.6 Various Types of Cyber Crime 359
19.6.1 Cyber Stalking 360
19.6.2 Sexual Harassment or Child Pornography 360
19.6.3 Forgery 360
19.6.4 Crime Related to Privacy of Software and Network Resources 360
19.6.5 Cyber Terrorism 360
19.6.6 Phishing, Vishing, and Smishing 360
19.6.7 Malfunction 361
19.6.8 Server Hacking 361
19.6.9 Spreading Virus 361
19.6.10 Spamming, Cross Site Scripting, and Web Jacking 361
19.7 Various Types of Cyber Attacks in Information Security 361
19.7.1 Web-Based Attacks in Information Security 362
19.7.2 System-Based Attacks in Information Security 364
19.8 Cybersecurity and Privacy Techniques 365
19.8.1 Authentication and Authorization 365
19.8.2 Cryptography 366
19.8.2.1 Symmetric Key Encryption 367
19.8.2.2 Asymmetric Key Encryption 367
19.8.3 Installation of Antivirus 367
19.8.4 Digital Signature 367
19.8.5 Firewall 369
19.8.6 Steganography 369
19.9 Essential Elements of Cybersecurity 370
19.10 Basic Security Concerns for Cybersecurity 371
19.10.1 Precaution 372
19.10.2 Maintenance 372
19.10.3 Reactions 373
19.11 Cybersecurity Layered Stack 373
19.12 Basic Security and Privacy Check List 374
19.13 Future Challenges of Cybersecurity 374
References 376
20 Changing the Conventional Banking System through Blockchain 379
Khushboo Tripathi, Neha Bhateja and Ashish Dhillon
20.1 Introduction 379
20.1.1 Introduction to Blockchain 379
20.1.2 Classification of Blockchains 381
20.1.2.1 Public Blockchain 381
20.1.2.2 Private Blockchain 382
20.1.2.3 Hybrid Blockchain 382
20.1.2.4 Consortium Blockchain 382
20.1.3 Need for Blockchain Technology 383
20.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary 383
20.1.4 Comparison of Blockchain and Cryptocurrency 384
20.1.4.1 Distributed Ledger Technology (DLT) 384
20.1.5 Types of Consensus Mechanism 385
20.1.5.1 Consensus Algorithm: A Quick Background 385
20.1.6 Proof of Work 386
20.1.7 Proof of Stake 387
20.1.7.1 Delegated Proof of Stake 387
20.1.7.2 Byzantine Fault Tolerance 388
20.2 Literature Survey 388
20.2.1 The History of Blockchain Technology 388
20.2.2 Early Years of Blockchain Technology: 1991-2008 389
20.2.2.1 Evolution of Blockchain: Phase 1--Transactions 389
20.2.2.2 Evolution of Blockchain: Phase 2--Contracts 390
20.2.2.3 Evolution of Blockchain: Phase 3--Applications 390
20.2.3 Literature Review 391
20.2.4 Analysis 392
20.3 Methodology and Tools 392
20.3.1 Methodology 392
20.3.2 Flow Chart 393
20.3.3 Tools and Configuration 394
20.4 Experiment 394
20.4.1 Steps of Implementation 394
20.4.2 Screenshots of Experiment 397
20.5 Results 398
20.6 Conclusion 400
20.7 Future Scope 401
20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises 401
References 402
21 A Secured Online Voting System by Using Blockchain as the Medium 405
Leslie Mark, Vasaki Ponnusamy, Arya Wicaksana, Basilius Bias Christyono and Moeljono Widjaja
21.1 Blockchain-Based Online Voting System 405
21.1.1 Introduction 405
21.1.2 Structure of a Block in a Blockchain System 406
21.1.3 Function of Segments in a Block of the Blockchain 406
21.1.4 SHA-256 Hashing on the Blockchain 407
21.1.5 Interaction Involved in Blockchain-Based Online Voting System 409
21.1.6 Online Voting System Using Blockchain - Framework 409
21.2 Literature Review 410
21.2.1 Literature Review Outline 410
21.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model 410
21.2.1.2 Online Voting System Based on Visual Cryptography 411
21.2.1.3 Online Voting System Using Biometric Security and Steganography 412
21.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption 414
21.2.1.5 An Online Voting System Based on a Secured Blockchain 416
21.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach 417
21.2.1.7 Online Voting System Using Iris Recognition 418
21.2.1.8 Online Voting System Based on NID and SIM 420
21.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography 422
21.2.1.10 Online Voting System Using Secret Sharing-Based Authentication 425
21.2.2 Comparing the Existing Online Voting System 427
References 430
22 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects 431
Abhinav Juneja, Sapna Juneja, Vikram Bali, Vishal Jain and Hemant Upadhyay
22.1 Introduction 431
22.2 Literature Review 432
22.3 Different Variants of Cybersecurity in Action 432
22.4 Importance of Cybersecurity in Action 433
22.5 Methods for Establishing a Strategy for Cybersecurity 434
22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity 434
22.7 Where AI Is Actually Required to Deal With Cybersecurity 437
22.8 Challenges for Cybersecurity in Current State of Practice 438
22.9 Conclusion 438
References 438
Index 443
Part 1: Internet of Things 1
1 Voyage of Internet of Things in the Ocean of Technology 3
Tejaskumar R. Ghadiyali, Bharat C. Patel and Manish M. Kayasth
1.1 Introduction 3
1.1.1 Characteristics of IoT 4
1.1.2 IoT Architecture 5
1.1.3 Merits and Demerits of IoT 6
1.2 Technological Evolution Toward IoT 7
1.3 IoT-Associated Technology 8
1.4 Interoperability in IoT 14
1.5 Programming Technologies in IoT 15
1.5.1 Arduino 15
1.5.2 Raspberry Pi 17
1.5.3 Python 18
1.6 IoT Applications 19
Conclusion 22
References 22
2 AI for Wireless Network Optimization: Challenges and Opportunities 25
Murad Abusubaih
2.1 Introduction to AI 25
2.2 Self-Organizing Networks 27
2.2.1 Operation Principle of Self-Organizing Networks 27
2.2.2 Self-Configuration 28
2.2.3 Self-Optimization 28
2.2.4 Self-Healing 28
2.2.5 Key Performance Indicators 29
2.2.6 SON Functions 29
2.3 Cognitive Networks 29
2.4 Introduction to Machine Learning 30
2.4.1 ML Types 31
2.4.2 Components of ML Algorithms 31
2.4.3 How do Machines Learn? 32
2.4.3.1 Supervised Learning 32
2.4.3.2 Unsupervised Learning 33
2.4.3.3 Semi-Supervised Learning 35
2.4.3.4 Reinforcement Learning 35
2.4.4 ML and Wireless Networks 36
2.5 Software-Defined Networks 36
2.5.1 SDN Architecture 37
2.5.2 The OpenFlow Protocol 38
2.5.3 SDN and ML 39
2.6 Cognitive Radio Networks 39
2.6.1 Sensing Methods 41
2.7 ML for Wireless Networks: Challenges and Solution Approaches 41
2.7.1 Cellular Networks 42
2.7.1.1 Energy Saving 42
2.7.1.2 Channel Access and Assignment 42
2.7.1.3 User Association and Load Balancing 43
2.7.1.4 Traffic Engineering 44
2.7.1.5 QoS/QoE Prediction 45
2.7.1.6 Security 45
2.7.2 Wireless Local Area Networks 46
2.7.2.1 Access Point Selection 47
2.7.2.2 Interference Mitigation 48
2.7.2.3 Channel Allocation and Channel Bonding 49
2.7.2.4 Latency Estimation and Frame Length Selection 49
2.7.2.5 Handover 49
2.7.3 Cognitive Radio Networks 50
References 50
3 An Overview on Internet of Things (IoT) Segments and Technologies 57
Amarjit Singh
3.1 Introduction 57
3.2 Features of IoT 59
3.3 IoT Sensor Devices 59
3.4 IoT Architecture 61
3.5 Challenges and Issues in IoT 62
3.6 Future Opportunities in IoT 63
3.7 Discussion 64
3.8 Conclusion 65
References 65
4 The Technological Shift: AI in Big Data and IoT 69
Deepti Sharma, Amandeep Singh and Sanyam Singhal
4.1 Introduction 69
4.2 Artificial Intelligence 71
4.2.1 Machine Learning 71
4.2.2 Further Development in the Domain of Artificial Intelligence 73i
4.2.3 Programming Languages for Artificial Intelligence 74
4.2.4 Outcomes of Artificial Intelligence 74
4.3 Big Data 75
4.3.1 Artificial Intelligence Methods for Big Data 77
4.3.2 Industry Perspective of Big Data 77
4.3.2.1 In Medical Field 78
4.3.2.2 In Meteorological Department 78
4.3.2.3 In Industrial/Corporate Applications and Analytics 79
4.3.2.4 In Education 79
4.3.2.5 In Astronomy 79
4.4 Internet of Things 80
4.4.1 Interconnection of IoT With AoT 81
4.4.2 Difference Between IIoT and IoT 81
4.4.3 Industrial Approach for IoT 82
4.5 Technical Shift in AI, Big Data, and IoT 82
4.5.1 Industries Shifting to AI-Enabled Big Data Analytics 83
4.5.2 Industries Shifting to AI-Powered IoT Devices 84
4.5.3 Statistical Data of These Shifts 84
4.6 Conclusion 85
References 86
5 IoT's Data Processing Using Spark 91
Ankita Bansal and Aditya Atri
5.1 Introduction 91
5.2 Introduction to Apache Spark 92
5.2.1 Advantages of Apache Spark 93
5.2.2 Apache Spark's Components 93
5.3 Apache Hadoop MapReduce 94
5.3.1 Limitations of MapReduce 94
5.4 Resilient Distributed Dataset (RDD) 95
5.4.1 Features and Limitations of RDDs 95
5.5 DataFrames 96
5.6 Datasets 97
5.7 Introduction to Spark SQL 98
5.7.1 Spark SQL Architecture 99
5.7.2 Spark SQL Libraries 100
5.8 SQL Context Class in Spark 100
5.9 Creating Dataframes 101
5.9.1 Operations on DataFrames 102
5.10 Aggregations 103
5.11 Running SQL Queries on Dataframes 103
5.12 Integration With RDDs 104
5.12.1 Inferring the Schema Using Reflection 104
5.12.2 Specifying the Schema Programmatically 104
5.13 Data Sources 104
5.13.1 JSON Datasets 105
5.13.2 Hive Tables 105
5.13.3 Parquet Files 106
5.14 Operations on Data Sources 106
5.15 Industrial Applications 107
5.16 Conclusion 108
References 108
6 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs 111
Tayyab Khan and Karan Singh
6.1 Introduction 111
6.1.1 Components of WSNs 113
6.1.2 Trust 115
6.1.3 Major Contribution 120
6.2 Related Work 121
6.3 Network Topology and Assumptions 122
6.4 Proposed Trust Model 122
6.4.1 CM to CM (Direct) Trust Evaluation Scheme 123
6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx,y(deltat)) 124
6.4.3 CH-to-CH Direct Trust Estimation 125
6.4.4 BS-to-CH Feedback Trust Calculation 125
6.5 Result and Analysis 126
6.5.1 Severity Analysis 126
6.5.2 Malicious Node Detection 127
6.6 Conclusion and Future Work 128
References 128
7 Smart Applications of IoT 131
Pradeep Kamboj, T. Ratha Jeyalakshmi, P. Thillai Arasu, S. Balamurali and A. Murugan
7.1 Introduction 131
7.2 Background 132
7.2.1 Enabling Technologies for Building Intelligent Infrastructure 132
7.3 Smart City 136
7.3.1 Benefits of a Smart City 137
7.3.2 Smart City Ecosystem 137
7.3.3 Challenges in Smart Cities 138
7.4 Smart Healthcare 139
7.4.1 Smart Healthcare Applications 140
7.4.2 Challenges in Healthcare 141
7.5 Smart Agriculture 142
7.5.1 Environment Agriculture Controlling 143
7.5.2 Advantages 143
7.5.3 Challenges 144
7.6 Smart Industries 145
7.6.1 Advantages 147
7.6.2 Challenges 148
7.7 Future Research Directions 149
7.8 Conclusions 149
References 149
8 Sensor-Based Irrigation System: Introducing Technology in Agriculture 153
Rohit Rastogi, Krishna Vir Singh, Mihir Rai, Kartik Sachdeva, Tarun Yadav and Harshit Gupta
8.1 Introduction 153
8.1.1 Technology in Agriculture 154
8.1.2 Use and Need for Low-Cost Technology in Agriculture 154
8.2 Proposed System 154
8.3 Flow Chart 157
8.4 Use Case 158
8.5 System Modules 158
8.5.1 Raspberry Pi 158
8.5.2 Arduino Uno 158
8.5.3 DHT 11 Humidity and Temperature Sensor 158
8.5.4 Soil Moisture Sensor 160
8.5.5 Solenoid Valve 160
8.5.6 Drip Irrigation Kit 160
8.5.7 433 MHz RF Module 160
8.5.8 Mobile Application 160
8.5.9 Testing Phase 161
8.6 Limitations 162
8.7 Suggestions 162
8.8 Future Scope 162
8.9 Conclusion 163
Acknowledgement 163
References 163
Suggested Additional Readings 164
Key Terms and Definitions 164
Appendix 165
Example Code 166
9 Artificial Intelligence: An Imaginary World of Machine 167
Bharat C. Patel, Manish M. Kaysth and Tejaskumar R. Ghadiyali
9.1 The Dawn of Artificial Intelligence 167
9.2 Introduction 169
9.3 Components of AI 170
9.3.1 Machine Reasoning 170
9.3.2 Natural Language Processing 171
9.3.3 Automated Planning 171
9.3.4 Machine Learning 171
9.4 Types of Artificial Intelligence 172
9.4.1 Artificial Narrow Intelligence 172
9.4.2 Artificial General Intelligence 173
9.4.3 Artificial Super Intelligence 174
9.5 Application Area of AI 175
9.6 Challenges in Artificial Intelligence 176
9.7 Future Trends in Artificial Intelligence 177
9.8 Practical Implementation of AI Application 179
References 182
10 Impact of Deep Learning Techniques in IoT 185
M. Chandra Vadhana, P. Shanthi Bala and Immanuel Zion Ramdinthara
10.1 Introduction 185
10.2 Internet of Things 186
10.2.1 Characteristics of IoT 187
10.2.2 Architecture of IoT 187
10.2.2.1 Smart Device/Sensor Layer 187
10.2.2.2 Gateways and Networks 187
10.2.2.3 Management Service Layer 188
10.2.2.4 Application Layer 188
10.2.2.5 Interoperability of IoT 188
10.2.2.6 Security Requirements at a Different Layer of IoT 190
10.2.2.7 Future Challenges for IoT 190
10.2.2.8 Privacy and Security 190
10.2.2.9 Cost and Usability 191
10.2.2.10 Data Management 191
10.2.2.11 Energy Preservation 191
10.2.2.12 Applications of IoT 191
10.2.2.13 Essential IoT Technologies 193
10.2.2.14 Enriching the Customer Value 195
10.2.2.15 Evolution of the Foundational IoT Technologies 196
10.2.2.16 Technical Challenges in the IoT Environment 196
10.2.2.17 Security Challenge 197
10.2.2.18 Chaos Challenge 197
10.2.2.19 Advantages of IoT 198
10.2.2.20 Disadvantages of IoT 198
10.3 Deep Learning 198
10.3.1 Models of Deep Learning 199
10.3.1.1 Convolutional Neural Network 199
10.3.1.2 Recurrent Neural Networks 199
10.3.1.3 Long Short-Term Memory 200
10.3.1.4 Autoencoders 200
10.3.1.5 Variational Autoencoders 201
10.3.1.6 Generative Adversarial Networks 201
10.3.1.7 Restricted Boltzmann Machine 201
10.3.1.8 Deep Belief Network 201
10.3.1.9 Ladder Networks 202
10.3.2 Applications of Deep Learning 202
10.3.2.1 Industrial Robotics 202
10.3.2.2 E-Commerce Industries 202
10.3.2.3 Self-Driving Cars 202
10.3.2.4 Voice-Activated Assistants 202
10.3.2.5 Automatic Machine Translation 202
10.3.2.6 Automatic Handwriting Translation 203
10.3.2.7 Predicting Earthquakes 203
10.3.2.8 Object Classification in Photographs 203
10.3.2.9 Automatic Game Playing 203
10.3.2.10 Adding Sound to Silent Movies 203
10.3.3 Advantages of Deep Learning 203
10.3.4 Disadvantages of Deep Learning 203
10.3.5 Deployment of Deep Learning in IoT 203
10.3.6 Deep Learning Applications in IoT 204
10.3.6.1 Image Recognition 204
10.3.6.2 Speech/Voice Recognition 204
10.3.6.3 Indoor Localization 204
10.3.6.4 Physiological and Psychological Detection 205
10.3.6.5 Security and Privacy 205
10.3.7 Deep Learning Techniques on IoT Devices 205
10.3.7.1 Network Compression 205
10.3.7.2 Approximate Computing 206
10.3.7.3 Accelerators 206
10.3.7.4 Tiny Motes 206
10.4 IoT Challenges on Deep Learning and Future Directions 206
10.4.1 Lack of IoT Dataset 206
10.4.2 Pre-Processing 207
10.4.3 Challenges of 6V's 207
10.4.4 Deep Learning Limitations 207
10.5 Future Directions of Deep Learning 207
10.5.1 IoT Mobile Data 207
10.5.2 Integrating Contextual Information 208
10.5.3 Online Resource Provisioning for IoT Analytics 208
10.5.4 Semi-Supervised Analytic Framework 208
10.5.5 Dependable and Reliable IoT Analytics 208
10.5.6 Self-Organizing Communication Networks 208
10.5.7 Emerging IoT Applications 208
10.5.7.1 Unmanned Aerial Vehicles 209
10.5.7.2 Virtual/Augmented Reality 209
10.5.7.3 Mobile Robotics 209
10.6 Common Datasets for Deep Learning in IoT 209
10.7 Discussion 209
10.8 Conclusion 211
References 211
Part 2: Artificial Intelligence in Healthcare 215
11 Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques 217
Toufique A. Soomro, Ahmed J. Afifi, Pardeep Kumar, Muhammad Usman Keerio, Saleem Ahmed and Ahmed Ali
11.1 Introduction 217
11.2 Existing Methods Review 221
11.3 Methodology 223
11.3.1 Architecture of Stride U-Net 223
11.3.2 Loss Function 225
11.4 Databases and Evaluation Metrics 225
11.4.1 CNN Implementation Details 226
11.5 Results and Analysis 227
11.5.1 Evaluation on DRIVE and STARE Databases 227
11.5.2 Comparative Analysis 227
11.6 Concluding Remarks 229
References 230
12 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review 235
Muhammad Ali Nizamani, Muhammad Ali Memon and Pirah Brohi
12.1 Introduction 235
12.2 Methodology 237
12.3 IoT in Mental Health 238
12.4 Mental Healthcare Applications and Services Based on IoT 238
12.5 Benefits of IoT in Mental Health 241
12.5.1 Reduction in Treatment Cost 241
12.5.2 Reduce Human Error 241
12.5.3 Remove Geographical Barriers 241
12.5.4 Less Paperwork and Documentation 241
12.5.5 Early Stage Detection of Chronic Disorders 241
12.5.6 Improved Drug Management 242
12.5.7 Speedy Medical Attention 242
12.5.8 Reliable Results of Treatment 242
12.6 Challenges in IoT-Based Mental Healthcare Applications 242
12.6.1 Scalability 242
12.6.2 Trust 242
12.6.3 Security and Privacy Issues 243
12.6.4 Interoperability Issues 243
12.6.5 Computational Limits 243
12.6.6 Memory Limitations 243
12.6.7 Communications Media 244
12.6.8 Devices Multiplicity 244
12.6.9 Standardization 244
12.6.10 IoT-Based Healthcare Platforms 244
12.6.11 Network Type 244
12.6.12 Quality of Service 245
12.7 Blockchain in IoT for Healthcare 245
12.8 Results and Discussion 246
12.9 Limitations of the Survey 247
12.10 Conclusion 247
References 247
13 Monitoring Technologies for Precision Health 251
Rehab A. Rayan and Imran Zafar
13.1 Introduction 251
13.2 Applications of Monitoring Technologies 252
13.2.1 Everyday Life Activities 253
13.2.2 Sleeping and Stress 253
13.2.3 Breathing Patterns and Respiration 254
13.2.4 Energy and Caloric Consumption 254
13.2.5 Diabetes, Cardiac, and Cognitive Care 254
13.2.6 Disability and Rehabilitation 254
13.2.7 Pregnancy and Post-Procedural Care 255
13.3 Limitations 255
13.3.1 Quality of Data and Reliability 255
13.3.2 Safety, Privacy, and Legal Concerns 256
13.4 Future Insights 256
13.4.1 Consolidating Frameworks 256
13.4.2 Monitoring and Intervention 256
13.4.3 Research and Development 257
13.5 Conclusions 257
References 257
14 Impact of Artificial Intelligence in Cardiovascular Disease 261
Mir Khan, Saleem Ahmed, Pardeep Kumar and Dost Muhammad Saqib Bhatti
14.1 Artificial Intelligence 261
14.2 Machine Learning 262
14.3 The Application of AI in CVD 263
14.3.1 Precision Medicine 263
14.3.2 Clinical Prediction 263
14.3.3 Cardiac Imaging Analysis 264
14.4 Future Prospect 264
14.5 PUAI and Novel Medical Mode 265
14.5.1 Phenomenon of PUAI 265
14.5.2 Novel Medical Model 266
14.6 Traditional Mode 266
14.6.1 Novel Medical Mode Plus PUAI 266
14.7 Representative Calculations of AI 268
14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis 268
References 270
15 Healthcare Transformation With Clinical Big Data Predictive Analytics 273
Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, Mumtaz Qabulio, Sumera Naz Pathan and Asia Khatoon Soomro
15.1 Introduction 273
15.1.1 Big Data in Health Sector 275
15.1.2 Data Structure Produced in Health Sectors 275
15.2 Big Data Challenges in Healthcare 276
15.2.1 Big Data in Computational Healthcare 276
15.2.2 Big Data Predictive Analytics in Healthcare 276
15.2.3 Big Data for Adapted Healthcare 277
15.3 Cloud Computing and Big Data in Healthcare 278
15.4 Big Data Healthcare and IoT 278
15.5 Wearable Devices for Patient Health Monitoring 282
15.6 Big Data and Industry 4.0 283
15.7 Conclusion 283
References 284
16 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats 287
Rohit Rastogi, Mamta Saxena, D.K. Chaturvedi, Mayank Gupta, Mukund Rastogi, Prajwal Srivatava, Mohit Jain, Pradeep Kumar, Ujjawal Sharma, Rohan Choudhary and Neha Gupta
16.1 Introduction 287
16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate 287
16.1.2 Precautionary Guidelines Followed in Indian Continent 288
16.1.3 Spiritual Guidelines in Indian Society 289
16.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India 289
16.1.4 Veda Vigyaan: Ancient Vedic Knowledge 289
16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon 289
16.1.6 The Yagya Samagri 290
16.2 Literature Survey 290
16.2.1 Technical Aspects of Yajna and Mantra Therapy 290
16.2.2 Mantra Chanting and Its Science 290
16.2.3 Yagya Medicine (Yagyopathy) 290
16.2.4 The Medicinal HavanSamagri Components 291
16.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases 291
16.2.5 Scientific Benefits of Havan 291
16.3 Experimental Setup Protocols With Results 292
16.3.1 Subject Sample Distribution 295
16.3.1.1 Area Wise Distribution 295
16.3.2 Conclusion and Discussion Through Experimental Work 295
16.4 Future Scope and Limitations 297
16.5 Novelty 298
16.6 Recommendations 298
16.7 Applications of Yajna Therapy 299
16.8 Conclusions 299
Acknowledgement 299
References 299
Key Terms and Definitions 304
17 Extraction of Depression Symptoms From Social Networks 307
Bhavna Chilwal and Amit Kumar Mishra
17.1 Introduction 307
17.1.1 Diagnosis and Treatments 309
17.2 Data Mining in Healthcare 310
17.2.1 Text Mining 310
17.3 Social Network Sites 311
17.4 Symptom Extraction Tool 312
17.4.1 Data Collection 313
17.4.2 Data Processing 313
17.4.3 Data Analysis 314
17.5 Sentiment Analysis 316
17.5.1 Emotion Analysis 318
17.5.2 Behavioral Analysis 318
17.6 Conclusion 319
References 320
Part 3: Cybersecurity 323
18 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations 325
C. Kaviyazhiny, P. Shanthi Bala and A.S. Gowri
18.1 Introduction 325
18.2 Characteristics of Fog Computing 326
18.3 Reference Architecture of Fog Computing 328
18.4 CISCO IOx Framework 329
18.5 Security Practices in CISCO IOx 330
18.5.1 Potential Attacks on IoT Architecture 330
18.5.2 Perception Layer (Sensing) 331
18.5.3 Network Layer 331
18.5.4 Service Layer (Support) 332
18.5.5 Application Layer (Interface) 333
18.6 Security Issues in Fog Computing 333
18.6.1 Virtualization Issues 333
18.6.2 Web Security Issues 334
18.6.3 Internal/External Communication Issues 335
18.6.4 Data Security Related Issues 336
18.6.5 Wireless Security Issues 337
18.6.6 Malware Protection 338
18.7 Machine Learning for Secure Fog Computing 338
18.7.1 Layer 1 Cloud 339
18.7.2 Layer 2 Fog Nodes For The Community 340
18.7.3 Layer 3 Fog Node for Their Neighborhood 340
18.7.4 Layer 4 Sensors 341
18.8 Existing Security Solution in Fog Computing 341
18.8.1 Privacy-Preserving in Fog Computing 341
18.8.2 Pseudocode for Privacy Preserving in Fog Computing 342
18.8.3 Pseudocode for Feature Extraction 343
18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature 343
18.8.5 Pseudocode for Encrypting Data 344
18.8.6 Pseudocode for Data Partitioning 344
18.8.7 Encryption Algorithms in Fog Computing 345
18.9 Recommendation and Future Enhancement 345
18.9.1 Data Encryption 345
18.9.2 Preventing from Cache Attacks 346
18.9.3 Network Monitoring 346
18.9.4 Malware Protection 347
18.9.5 Wireless Security 347
18.9.6 Secured Vehicular Network 347
18.9.7 Secure Multi-Tenancy 348
18.9.8 Backup and Recovery 348
18.9.9 Security with Performance 348
18.10 Conclusion 349
References 349
19 Cybersecurity and Privacy Fundamentals 353
Ravi Verma
19.1 Introduction 353
19.2 Historical Background and Evolution of Cyber Crime 354
19.3 Introduction to Cybersecurity 355
19.3.1 Application Security 356
19.3.2 Information Security 356
19.3.3 Recovery From Failure or Disaster 356
19.3.4 Network Security 357
19.4 Classification of Cyber Crimes 357
19.4.1 Internal Attacks 357
19.4.2 External Attacks 358
19.4.3 Unstructured Attack 358
19.4.4 Structured Attack 358
19.5 Reasons Behind Cyber Crime 358
19.5.1 Making Money 359
19.5.2 Gaining Financial Growth and Reputation 359
19.5.3 Revenge 359
19.5.4 For Making Fun 359
19.5.5 To Recognize 359
19.5.6 Business Analysis and Decision Making 359
19.6 Various Types of Cyber Crime 359
19.6.1 Cyber Stalking 360
19.6.2 Sexual Harassment or Child Pornography 360
19.6.3 Forgery 360
19.6.4 Crime Related to Privacy of Software and Network Resources 360
19.6.5 Cyber Terrorism 360
19.6.6 Phishing, Vishing, and Smishing 360
19.6.7 Malfunction 361
19.6.8 Server Hacking 361
19.6.9 Spreading Virus 361
19.6.10 Spamming, Cross Site Scripting, and Web Jacking 361
19.7 Various Types of Cyber Attacks in Information Security 361
19.7.1 Web-Based Attacks in Information Security 362
19.7.2 System-Based Attacks in Information Security 364
19.8 Cybersecurity and Privacy Techniques 365
19.8.1 Authentication and Authorization 365
19.8.2 Cryptography 366
19.8.2.1 Symmetric Key Encryption 367
19.8.2.2 Asymmetric Key Encryption 367
19.8.3 Installation of Antivirus 367
19.8.4 Digital Signature 367
19.8.5 Firewall 369
19.8.6 Steganography 369
19.9 Essential Elements of Cybersecurity 370
19.10 Basic Security Concerns for Cybersecurity 371
19.10.1 Precaution 372
19.10.2 Maintenance 372
19.10.3 Reactions 373
19.11 Cybersecurity Layered Stack 373
19.12 Basic Security and Privacy Check List 374
19.13 Future Challenges of Cybersecurity 374
References 376
20 Changing the Conventional Banking System through Blockchain 379
Khushboo Tripathi, Neha Bhateja and Ashish Dhillon
20.1 Introduction 379
20.1.1 Introduction to Blockchain 379
20.1.2 Classification of Blockchains 381
20.1.2.1 Public Blockchain 381
20.1.2.2 Private Blockchain 382
20.1.2.3 Hybrid Blockchain 382
20.1.2.4 Consortium Blockchain 382
20.1.3 Need for Blockchain Technology 383
20.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary 383
20.1.4 Comparison of Blockchain and Cryptocurrency 384
20.1.4.1 Distributed Ledger Technology (DLT) 384
20.1.5 Types of Consensus Mechanism 385
20.1.5.1 Consensus Algorithm: A Quick Background 385
20.1.6 Proof of Work 386
20.1.7 Proof of Stake 387
20.1.7.1 Delegated Proof of Stake 387
20.1.7.2 Byzantine Fault Tolerance 388
20.2 Literature Survey 388
20.2.1 The History of Blockchain Technology 388
20.2.2 Early Years of Blockchain Technology: 1991-2008 389
20.2.2.1 Evolution of Blockchain: Phase 1--Transactions 389
20.2.2.2 Evolution of Blockchain: Phase 2--Contracts 390
20.2.2.3 Evolution of Blockchain: Phase 3--Applications 390
20.2.3 Literature Review 391
20.2.4 Analysis 392
20.3 Methodology and Tools 392
20.3.1 Methodology 392
20.3.2 Flow Chart 393
20.3.3 Tools and Configuration 394
20.4 Experiment 394
20.4.1 Steps of Implementation 394
20.4.2 Screenshots of Experiment 397
20.5 Results 398
20.6 Conclusion 400
20.7 Future Scope 401
20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises 401
References 402
21 A Secured Online Voting System by Using Blockchain as the Medium 405
Leslie Mark, Vasaki Ponnusamy, Arya Wicaksana, Basilius Bias Christyono and Moeljono Widjaja
21.1 Blockchain-Based Online Voting System 405
21.1.1 Introduction 405
21.1.2 Structure of a Block in a Blockchain System 406
21.1.3 Function of Segments in a Block of the Blockchain 406
21.1.4 SHA-256 Hashing on the Blockchain 407
21.1.5 Interaction Involved in Blockchain-Based Online Voting System 409
21.1.6 Online Voting System Using Blockchain - Framework 409
21.2 Literature Review 410
21.2.1 Literature Review Outline 410
21.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model 410
21.2.1.2 Online Voting System Based on Visual Cryptography 411
21.2.1.3 Online Voting System Using Biometric Security and Steganography 412
21.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption 414
21.2.1.5 An Online Voting System Based on a Secured Blockchain 416
21.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach 417
21.2.1.7 Online Voting System Using Iris Recognition 418
21.2.1.8 Online Voting System Based on NID and SIM 420
21.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography 422
21.2.1.10 Online Voting System Using Secret Sharing-Based Authentication 425
21.2.2 Comparing the Existing Online Voting System 427
References 430
22 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects 431
Abhinav Juneja, Sapna Juneja, Vikram Bali, Vishal Jain and Hemant Upadhyay
22.1 Introduction 431
22.2 Literature Review 432
22.3 Different Variants of Cybersecurity in Action 432
22.4 Importance of Cybersecurity in Action 433
22.5 Methods for Establishing a Strategy for Cybersecurity 434
22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity 434
22.7 Where AI Is Actually Required to Deal With Cybersecurity 437
22.8 Challenges for Cybersecurity in Current State of Practice 438
22.9 Conclusion 438
References 438
Index 443
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).
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).
