John Wiley & Sons The Smart Cyber Ecosystem for Sustainable Development Cover The Smart Cyber Ecosystem for Sustainable Development As the entire ecosystem is moving towards a s.. Product #: 978-1-119-76164-8 Regular price: $195.33 $195.33 In Stock

The Smart Cyber Ecosystem for Sustainable Development

Kumar, Pardeep / Jain, Vishal / Ponnusamy, Vasaki (Editor)

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1. Edition November 2021
480 Pages, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-76164-8
John Wiley & Sons

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The Smart Cyber Ecosystem for Sustainable Development

As the entire ecosystem is moving towards a sustainable goal, technology driven smart cyber system is the enabling factor to make this a success, and the current book documents how this can be attained.

The cyber ecosystem consists of a huge number of different entities that work and interact with each other in a highly diversified manner. In this era, when the world is surrounded by many unseen challenges and when its population is increasing and resources are decreasing, scientists, researchers, academicians, industrialists, government agencies and other stakeholders are looking toward smart and intelligent cyber systems that can guarantee sustainable development for a better and healthier ecosystem. The main actors of this cyber ecosystem include the Internet of Things (IoT), artificial intelligence (AI), and the mechanisms providing cybersecurity.

This book attempts to collect and publish innovative ideas, emerging trends, implementation experiences, and pertinent user cases for the purpose of serving mankind and societies with sustainable societal development. The 22 chapters of the book are divided into three sections: Section I deals with the Internet of Things, Section II focuses on artificial intelligence and especially its applications in healthcare, whereas Section III investigates the different cyber security mechanisms.

Audience

This book will attract researchers and graduate students working in the areas of artificial intelligence, blockchain, Internet of Things, information technology, as well as industrialists, practitioners, technology developers, entrepreneurs, and professionals who are interested in exploring, designing and implementing these technologies.

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

COPYRIGHTED MATERIAL

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).