The smart cyber ecosystem for sustainable development /
edited by Pardeep Kumar, Vishal Jain, Vasaki Ponnusamy.
- 1st.
- 1 online resource
Preface -- Part 1: Internet of Things -- 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 -- 1.1.1 Characteristics of IoT -- 1.1.2 IoT Architecture -- 1.1.3 Merits and Demerits of IoT -- 1.2 Technological Evolution Toward IoT -- 1.3 IoT-Associated Technology -- 1.4 Interoperability in IoT -- 1.5 Programming Technologies in IoT -- 1.5.1 Arduino -- 1.5.2 Raspberry Pi -- 1.5.3 Python -- 1.6 IoT Applications -- Conclusion -- References -- 2 AI for Wireless Network Optimization: Challenges and Opportunities 25 Murad Abusubaih -- 2.1 Introduction to AI -- 2.2 Self-Organizing Networks -- 2.2.1 Operation Principle of Self-Organizing Networks -- 2.2.2 Self-Configuration -- 2.2.3 Self-Optimization -- 2.2.4 Self-Healing -- 2.2.5 Key Performance Indicators -- 2.2.6 SON Functions -- 2.3 Cognitive Networks -- 2.4 Introduction to Machine Learning -- 2.4.1 ML Types -- 2.4.2 Components of ML Algorithms -- 2.4.3 How do Machines Learn? -- 2.4.3.1 Supervised Learning -- 2.4.3.2 Unsupervised Learning -- 2.4.3.3 Semi-Supervised Learning -- 2.4.3.4 Reinforcement Learning -- 2.4.4 ML and Wireless Networks -- 2.5 Software-Defined Networks -- 2.5.1 SDN Architecture -- 2.5.2 The OpenFlow Protocol -- 2.5.3 SDN and ML -- 2.6 Cognitive Radio Networks -- 2.6.1 Sensing Methods -- 2.7 ML for Wireless Networks: Challenges and Solution Approaches -- 2.7.1 Cellular Networks -- 2.7.1.1 Energy Saving -- 2.7.1.2 Channel Access and Assignment -- 2.7.1.3 User Association and Load Balancing -- 2.7.1.4 Traffic Engineering -- 2.7.1.5 QoS/QoE Prediction -- 2.7.1.6 Security -- 2.7.2 Wireless Local Area Networks -- 2.7.2.1 Access Point Selection -- 2.7.2.2 Interference Mitigation -- 2.7.2.3 Channel Allocation and Channel Bonding -- 2.7.2.4 Latency Estimation and Frame Length Selection -- 2.7.2.5 Handover -- 2.7.3 Cognitive Radio Networks -- References -- 3 An Overview on Internet of Things (IoT) Segments and Technologies 57 Amarjit Singh -- 3.1 Introduction -- 3.2 Features of IoT -- 3.3 IoT Sensor Devices -- 3.4 IoT Architecture -- 3.5 Challenges and Issues in IoT -- 3.6 Future Opportunities in IoT -- 3.7 Discussion -- 3.8 Conclusion -- References -- 4 The Technological Shift: AI in Big Data and IoT 69 Deepti Sharma, Amandeep Singh and Sanyam Singhal -- 4.1 Introduction -- 4.2 Artificial Intelligence -- 4.2.1 Machine Learning -- 4.2.2 Further Development in the Domain of Artificial Intelligence -- 4.2.3 Programming Languages for Artificial Intelligence -- 4.2.4 Outcomes of Artificial Intelligence -- 4.3 Big Data -- 4.3.1 Artificial Intelligence Methods for Big Data -- 4.3.2 Industry Perspective of Big Data -- 4.3.2.1 In Medical Field -- 4.3.2.2 In Meteorological Department -- 4.3.2.3 In Industrial/Corporate Applications and Analytics -- 4.3.2.4 In Education -- 4.3.2.5 In Astronomy -- 4.4 Internet of Things -- 4.4.1 Interconnection of IoT With AoT -- 4.4.2 Difference Between IIoT and IoT -- 4.4.3 Industrial Approach for IoT -- 4.5 Technical Shift in AI, Big Data, and IoT -- 4.5.1 Industries Shifting to AI-Enabled Big Data Analytics -- 4.5.2 Industries Shifting to AI-Powered IoT Devices -- 4.5.3 Statistical Data of These Shifts -- 4.6 Conclusion -- References -- 5 IoT's Data Processing Using Spark 91 Ankita Bansal and Aditya Atri -- 5.1 Introduction -- 5.2 Introduction to Apache Spark -- 5.2.1 Advantages of Apache Spark -- 5.2.2 Apache Spark's Components -- 5.3 Apache Hadoop MapReduce -- 5.3.1 Limitations of MapReduce -- 5.4 Resilient Distributed Dataset (RDD) -- 5.4.1 Features and Limitations of RDDs -- 5.5 DataFrames -- 5.6 Datasets -- 5.7 Introduction to Spark SQL -- 5.7.1 Spark SQL Architecture -- 5.7.2 Spark SQL Libraries -- 5.8 SQL Context Class in Spark -- 5.9 Creating Dataframes -- 5.9.1 Operations on DataFrames -- 5.10 Aggregations -- 5.11 Running SQL Queries on Dataframes -- 5.12 Integration With RDDs -- 5.12.1 Inferring the Schema Using Reflection -- 5.12.2 Specifying the Schema Programmatically -- 5.13 Data Sources -- 5.13.1 JSON Datasets -- 5.13.2 Hive Tables -- 5.13.3 Parquet Files -- 5.14 Operations on Data Sources -- 5.15 Industrial Applications -- 5.16 Conclusion -- References -- 6 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs 111 Tayyab Khan and Karan Singh -- 6.1 Introduction -- 6.1.1 Components of WSNs -- 6.1.2 Trust -- 6.1.3 Major Contribution -- 6.2 Related Work -- 6.3 Network Topology and Assumptions -- 6.4 Proposed Trust Model -- 6.4.1 CM to CM (Direct) Trust Evaluation Scheme -- 6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx,y(t)) -- 6.4.3 CH-to-CH Direct Trust Estimation -- 6.4.4 BS-to-CH Feedback Trust Calculation -- 6.5 Result and Analysis -- 6.5.1 Severity Analysis -- 6.5.2 Malicious Node Detection -- 6.6 Conclusion and Future Work -- References -- 7 Smart Applications of IoT 131 Pradeep Kamboj, T. Ratha Jeyalakshmi, P. Thillai Arasu, S. Balamurali and A. Murugan -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Enabling Technologies for Building Intelligent Infrastructure -- 7.3 Smart City -- 7.3.1 Benefits of a Smart City -- 7.3.2 Smart City Ecosystem -- 7.3.3 Challenges in Smart Cities -- 7.4 Smart Healthcare -- 7.4.1 Smart Healthcare Applications -- 7.4.2 Challenges in Healthcare -- 7.5 Smart Agriculture -- 7.5.1 Environment Agriculture Controlling -- 7.5.2 Advantages -- 7.5.3 Challenges -- 7.6 Smart Industries -- 7.6.1 Advantages -- 7.6.2 Challenges -- 7.7 Future Research Directions -- 7.8 Conclusions -- References -- 8 Sensor-Based Irrigation System: Introducing Technology in Agriculture 153 Rohit Rastogi, Krishna Vir Singh, Mihir Rai, Kartik Sachdeva, Tarun Yadav and Harshit Gupta -- 8.1 Introduction -- 8.1.1 Technology in Agriculture -- 8.1.2 Use and Need for Low-Cost Technology in Agriculture -- 8.2 Proposed System -- 8.3 Flow Chart -- 8.4 Use Case -- 8.5 System Modules -- 8.5.1 Raspberry Pi -- 8.5.2 Arduino Uno -- 8.5.3 DHT 11 Humidity and Temperature Sensor -- 8.5.4 Soil Moisture Sensor -- 8.5.5 Solenoid Valve -- 8.5.6 Drip Irrigation Kit -- 8.5.7 433 MHz RF Module -- 8.5.8 Mobile Application -- 8.5.9 Testing Phase -- 8.6 Limitations -- 8.7 Suggestions -- 8.8 Future Scope -- 8.9 Conclusion -- Acknowledgement -- References -- Suggested Additional Readings -- Key Terms and Definitions -- Appendix -- Example Code -- 9 Artificial Intelligence: An Imaginary World of Machine 167 Bharat C. Patel, Manish M. Kaysth and Tejaskumar R. Ghadiyali -- 9.1 The Dawn of Artificial Intelligence -- 9.2 Introduction -- 9.3 Components of AI -- 9.3.1 Machine Reasoning -- 9.3.2 Natural Language Processing -- 9.3.3 Automated Planning -- 9.3.4 Machine Learning -- 9.4 Types of Artificial Intelligence -- 9.4.1 Artificial Narrow Intelligence -- 9.4.2 Artificial General Intelligence -- 9.4.3 Artificial Super Intelligence -- 9.5 Application Area of AI -- 9.6 Challenges in Artificial Intelligence -- 9.7 Future Trends in Artificial Intelligence -- 9.8 Practical Implementation of AI Application -- References -- 10 Impact of Deep Learning Techniques in IoT 185 M. Chandra Vadhana, P. Shanthi Bala and Immanuel Zion Ramdinthara -- 10.1 Introduction -- 10.2 Internet of Things -- 10.2.1 Characteristics of IoT -- 10.2.2 Architecture of IoT -- 10.2.2.1 Smart Device/Sensor Layer -- 10.2.2.2 Gateways and Networks -- 10.2.2.3 Management Service Layer -- 10.2.2.4 Application Layer -- 10.2.2.5 Interoperability of IoT -- 10.2.2.6 Security Requirements at a Different Layer of IoT -- 10.2.2.7 Future Challenges for IoT -- 10.2.2.8 Privacy and Security -- 10.2.2.9 Cost and Usability -- 10.2.2.10 Data Management -- 10.2.2.11 Energy Preservation -- 10.2.2.12 Applications of IoT -- 10.2.2.13 Essential IoT Technologies -- 10.2.2.14 Enriching the Customer Value -- 10.2.2.15 Evolution of the Foundational IoT Technologies -- 10.2.2.16 Technical Challenges in the IoT Environment -- 10.2.2.17 Security Challenge -- 10.2.2.18 Chaos Challenge -- 10.2.2.19 Advantages of IoT -- 10.2.2.20 Disadvantages of IoT -- 10.3 Deep Learning -- 10.3.1 Models of Deep Learn. ing -- 10.3.1.1 Convolutional Neural Network -- 10.3.1.2 Recurrent Neural Networks -- 10.3.1.3 Long Short-Term Memory -- 10.3.1.4 Autoencoders -- 10.3.1.5 Variational Autoencoders -- 10.3.1.6 Generative Adversarial Networks -- 10.3.1.7 Restricted Boltzmann Machine -- 10.3.1.8 Deep Belief Network -- 10.3.1.9 Ladder Networks -- 10.3.2 Applications of Deep Learning -- 10.3.2.1 Industrial Robotics -- 10.3.2.2 E-Commerce Industries -- 10.3.2.3 Self-Driving Cars -- 10.3.2.4 Voice-Activated Assistants -- 10.3.2.5 Automatic Machine Translation -- 10.3.2.6 Automatic Handwriting Translation -- 10.3.2.7 Predicting Earthquakes -- 10.3.2.8 Object Classification in Photographs -- 10.3.2.9 Automatic Game Playing -- 10.3.2.10 Adding Sound to Silent Movies -- 10.3.3 Advantages of Deep Learning -- 10.3.4 Disadvantages of Deep Learning -- 10.3.5 Deployment of Deep Learning in IoT -- 10.3.6 Deep Learning Applications in IoT -- 10.3.6.1 Image Recognition -- 10.3.6.2 Speech/Voice Recognition -- 10.3.6.3 Indoor Localization -- 10.3.6.4 Physiological and Psychological Detection -- 10.3.6.5 Security and Privacy -- 10.3.7 Deep Learning Techniques on IoT Devices -- 10.3.7.1 Network Compression -- 10.3.7.2 Approximate Computing -- 10.3.7.3 Accelerators -- 10.3.7.4 Tiny Motes -- 10.4 IoT Challenges on Deep Learning and Future Directions -- 10.4.1 Lack of IoT Dataset -- 10.4.2 Pre-Processing -- 10.4.3 Challenges of 6V's -- 10.4.4 Deep Learning Limitations -- 10.5 Future Directions of Deep Learning -- 10.5.1 IoT Mobile Data -- 10.5.2 Integrating Contextual Information -- 10.5.3 Online Resource Provisioning for IoT Analytics -- 10.5.4 Semi-Supervised Analytic Framework -- 10.5.5 Dependable and Reliable IoT Analytics -- 10.5.6 Self-Organizing Communication Networks -- 10.5.7 Emerging IoT Applications -- 10.5.7.1 Unmanned Aerial Vehicles -- 10.5.7.2 Virtual/Augmented Reality -- 10.5.7.3 Mobile Robotics -- 10.6 Common Datasets for Deep Learning in IoT -- 10.7 Discussion -- 10.8 Conclusion -- References -- Part 2: Artificial Intelligence in Healthcare -- 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 -- 11.2 Existing Methods Review -- 11.3 Methodology -- 11.3.1 Architecture of Stride U-Net -- 11.3.2 Loss Function -- 11.4 Databases and Evaluation Metrics -- 11.4.1 CNN Implementation Details -- 11.5 Results and Analysis -- 11.5.1 Evaluation on DRIVE and STARE Databases -- 11.5.2 Comparative Analysis -- 11.6 Concluding Remarks -- References -- 12 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review 235 Muhammad Ali Nizamani, Muhammad Ali Memon and Pirah Brohi -- 12.1 Introduction -- 12.2 Methodology -- 12.3 IoT in Mental Health -- 12.4 Mental Healthcare Applications and Services Based on IoT -- 12.5 Benefits of IoT in Mental Health -- 12.5.1 Reduction in Treatment Cost -- 12.5.2 Reduce Human Error -- 12.5.3 Remove Geographical Barriers -- 12.5.4 Less Paperwork and Documentation -- 12.5.5 Early Stage Detection of Chronic Disorders -- 12.5.6 Improved Drug Management -- 12.5.7 Speedy Medical Attention -- 12.5.8 Reliable Results of Treatment -- 12.6 Challenges in IoT-Based Mental Healthcare Applications -- 12.6.1 Scalability -- 12.6.2 Trust -- 12.6.3 Security and Privacy Issues -- 12.6.4 Interoperability Issues -- 12.6.5 Computational Limits -- 12.6.6 Memory Limitations -- 12.6.7 Communications Media -- 12.6.8 Devices Multiplicity -- 12.6.9 Standardization -- 12.6.10 IoT-Based Healthcare Platforms -- 12.6.11 Network Type -- 12.6.12 Quality of Service -- 12.7 Blockchain in IoT for Healthcare -- 12.8 Results and Discussion -- 12.9 Limitations of the Survey -- 12.10 Conclusion -- References -- 13 Monitoring Technologies for Precision Health 251 Rehab A. Rayan and Imran Zafar -- 13.1 Introduction -- 13.2 Applications of Monitoring Technologies -- 13.2.1 Everyday Life Activities -- 13.2.2 Sleeping and Stress -- 13.2.3 Breathing Patterns and Respiration -- 13.2.4 Energy and Caloric Consumption -- 13.2.5 Diabetes, Cardiac, and Cognitive Care -- 13.2.6 Disability and Rehabilitation -- 13.2.7 Pregnancy and Post-Procedural Care -- 13.3 Limitations -- 13.3.1 Quality of Data and Reliability -- 13.3.2 Safety, Privacy, and Legal Concerns -- 13.4 Future Insights -- 13.4.1 Consolidating Frameworks -- 13.4.2 Monitoring and Intervention -- 13.4.3 Research and Development -- 13.5 Conclusions -- References -- 14 Impact of Artificial Intelligence in Cardiovascular Disease 261 Mir Khan, Saleem Ahmed, Pardeep Kumar and Dost Muhammad Saqib Bhatti -- 14.1 Artificial Intelligence -- 14.2 Machine Learning -- 14.3 The Application of AI in CVD -- 14.3.1 Precision Medicine -- 14.3.2 Clinical Prediction -- 14.3.3 Cardiac Imaging Analysis -- 14.4 Future Prospect -- 14.5 PUAI and Novel Medical Mode -- 14.5.1 Phenomenon of PUAI -- 14.5.2 Novel Medical Model -- 14.6 Traditional Mode -- 14.6.1 Novel Medical Mode Plus PUAI -- 14.7 Representative Calculations of AI -- 14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis -- References -- 15 Healthcare Transformation With Clinical Big Data Predictive Analytics 273 Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, Mumtaz Qabulio, Sumera Naz Pathan and Asia Khatoon Soomro -- 15.1 Introduction -- 15.1.1 Big Data in Health Sector -- 15.1.2 Data Structure Produced in Health Sectors -- 15.2 Big Data Challenges in Healthcare -- 15.2.1 Big Data in Computational Healthcare -- 15.2.2 Big Data Predictive Analytics in Healthcare -- 15.2.3 Big Data for Adapted Healthcare -- 15.3 Cloud Computing and Big Data in Healthcare -- 15.4 Big Data Healthcare and IoT -- 15.5 Wearable Devices for Patient Health Monitoring -- 15.6 Big Data and Industry 4.0 -- 15.7 Conclusion -- References -- 16 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats 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 -- 16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate -- 16.1.2 Precautionary Guidelines Followed in Indian Continent -- 16.1.3 Spiritual Guidelines in Indian Society -- 16.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India -- 16.1.4 Veda Vigyaan: Ancient Vedic Knowledge -- 16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon -- 16.1.6 The Yagya Samagri -- 16.2 Literature Survey -- 16.2.1 Technical Aspects of Yajna and Mantra Therapy -- 16.2.2 Mantra Chanting and Its Science -- 16.2.3 Yagya Medicine (Yagyopathy) -- 16.2.4 The Medicinal HavanSamagri Components -- 16.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases -- 16.2.5 Scientific Benefits of Havan -- 16.3 Experimental Setup Protocols With Results -- 16.3.1 Subject Sample Distribution -- 16.3.1.1 Area Wise Distribution -- 16.3.2 Conclusion and Discussion Through Experimental Work -- 16.4 Future Scope and Limitations -- 16.5 Novelty -- 16.6 Recommendations -- 16.7 Applications of Yajna Therapy -- 16.8 Conclusions -- Acknowledgement -- References -- Key Terms and Definitions -- 17 Extraction of Depression Symptoms From Social Networks 307 Bhavna Chilwal and Amit Kumar Mishra -- 17.1 Introduction -- 17.1.1 Diagnosis and Treatments -- 17.2 Data Mining in Healthcare -- 17.2.1 Text Mining -- 17.3 Social Network Sites -- 17.4 Symptom Extraction Tool -- 17.4.1 Data Collection -- 17.4.2 Data Processing -- 17.4.3 Data Analysis -- 17.5 Sentiment Analysis -- 17.5.1 Emotion Analysis -- 17.5.2 Behavioral Analysis -- 17.6 Conclusion -- References -- Part 3: Cybersecurity -- 18 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations 325 C. Kaviyazhiny, P. Shanthi Bala and A.S. Gowri -- 18.1 Introduction -- 18.2 Characteristics of Fog Computing -- 18.3 Reference Architecture of Fog Computing -- 18.4 CISCO IOx Framework -- 18.5 Security Practices in CISCO IOx -- 18.5.1 Potential Attacks on IoT Architecture -- 18.5.2 Perception Layer (S. ensing) -- 18.5.3 Network Layer -- 18.5.4 Service Layer (Support) -- 18.5.5 Application Layer (Interface) -- 18.6 Security Issues in Fog Computing -- 18.6.1 Virtualization Issues -- 18.6.2 Web Security Issues -- 18.6.3 Internal/External Communication Issues -- 18.6.4 Data Security Related Issues -- 18.6.5 Wireless Security Issues -- 18.6.6 Malware Protection -- 18.7 Machine Learning for Secure Fog Computing -- 18.7.1 Layer 1 Cloud -- 18.7.2 Layer 2 Fog Nodes For The Community -- 18.7.3 Layer 3 Fog Node for Their Neighborhood -- 18.7.4 Layer 4 Sensors -- 18.8 Existing Security Solution in Fog Computing -- 18.8.1 Privacy-Preserving in Fog Computing -- 18.8.2 Pseudocode for Privacy Preserving in Fog Computing -- 18.8.3 Pseudocode for Feature Extraction -- 18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature -- 18.8.5 Pseudocode for Encrypting Data -- 18.8.6 Pseudocode for Data Partitioning -- 18.8.7 Encryption Algorithms in Fog Computing -- 18.9 Recommendation and Future Enhancement -- 18.9.1 Data Encryption -- 18.9.2 Preventing from Cache Attacks -- 18.9.3 Network Monitoring -- 18.9.4 Malware Protection -- 18.9.5 Wireless Security -- 18.9.6 Secured Vehicular Network -- 18.9.7 Secure Multi-Tenancy -- 18.9.8 Backup and Recovery -- 18.9.9 Security with Performance -- 18.10 Conclusion -- References -- 19 Cybersecurity and Privacy Fundamentals 353 Ravi Verma -- 19.1 Introduction -- 19.2 Historical Background and Evolution of Cyber Crime -- 19.3 Introduction to Cybersecurity -- 19.3.1 Application Security -- 19.3.2 Information Security -- 19.3.3 Recovery From Failure or Disaster -- 19.3.4 Network Security -- 19.4 Classification of Cyber Crimes -- 19.4.1 Internal Attacks -- 19.4.2 External Attacks -- 19.4.3 Unstructured Attack -- 19.4.4 Structured Attack -- 19.5 Reasons Behind Cyber Crime -- 19.5.1 Making Money -- 19.5.2 Gaining Financial Growth and Reputation -- 19.5.3 Revenge -- 19.5.4 For Making Fun -- 19.5.5 To Recognize -- 19.5.6 Business Analysis and Decision Making -- 19.6 Various Types of Cyber Crime -- 19.6.1 Cyber Stalking -- 19.6.2 Sexual Harassment or Child Pornography -- 19.6.3 Forgery -- 19.6.4 Crime Related to Privacy of Software and Network Resources -- 19.6.5 Cyber Terrorism -- 19.6.6 Phishing, Vishing, and Smishing -- 19.6.7 Malfunction -- 19.6.8 Server Hacking -- 19.6.9 Spreading Virus -- 19.6.10 Spamming, Cross Site Scripting, and Web Jacking -- 19.7 Various Types of Cyber Attacks in Information Security -- 19.7.1 Web-Based Attacks in Information Security -- 19.7.2 System-Based Attacks in Information Security -- 19.8 Cybersecurity and Privacy Techniques -- 19.8.1 Authentication and Authorization -- 19.8.2 Cryptography -- 19.8.2.1 Symmetric Key Encryption -- 19.8.2.2 Asymmetric Key Encryption -- 19.8.3 Installation of Antivirus -- 19.8.4 Digital Signature -- 19.8.5 Firewall -- 19.8.6 Steganography -- 19.9 Essential Elements of Cybersecurity -- 19.10 Basic Security Concerns for Cybersecurity -- 19.10.1 Precaution -- 19.10.2 Maintenance -- 19.10.3 Reactions -- 19.11 Cybersecurity Layered Stack -- 19.12 Basic Security and Privacy Check List -- 19.13 Future Challenges of Cybersecurity -- References -- 20 Changing the Conventional Banking System through Blockchain 379 Khushboo Tripathi, Neha Bhateja and Ashish Dhillon -- 20.1 Introduction -- 20.1.1 Introduction to Blockchain -- 20.1.2 Classification of Blockchains -- 20.1.2.1 Public Blockchain -- 20.1.2.2 Private Blockchain -- 20.1.2.3 Hybrid Blockchain -- 20.1.2.4 Consortium Blockchain -- 20.1.3 Need for Blockchain Technology -- 20.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary -- 20.1.4 Comparison of Blockchain and Cryptocurrency -- 20.1.4.1 Distributed Ledger Technology (DLT) -- 20.1.5 Types of Consensus Mechanism -- 20.1.5.1 Consensus Algorithm: A Quick Background -- 20.1.6 Proof of Work -- 20.1.7 Proof of Stake -- 20.1.7.1 Delegated Proof of Stake -- 20.1.7.2 Byzantine Fault Tolerance -- 20.2 Literature Survey -- 20.2.1 The History of Blockchain Technology -- 20.2.2 Early Years of Blockchain Technology: 1991-2008 -- 20.2.2.1 Evolution of Blockchain: Phase 1--Transactions -- 20.2.2.2 Evolution of Blockchain: Phase 2--Contracts -- 20.2.2.3 Evolution of Blockchain: Phase 3--Applications -- 20.2.3 Literature Review -- 20.2.4 Analysis -- 20.3 Methodology and Tools -- 20.3.1 Methodology -- 20.3.2 Flow Chart -- 20.3.3 Tools and Configuration -- 20.4 Experiment -- 20.4.1 Steps of Implementation -- 20.4.2 Screenshots of Experiment -- 20.5 Results -- 20.6 Conclusion -- 20.7 Future Scope -- 20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises -- References -- 21 A Secured Online Voting System by Using Blockchain as the Medium 405 Leslie Mark, Vasaki Ponnusamy, Arya Wicaksana, Basilius Bias Christyono and Moeljono Widjaja -- 21.1 Blockchain-Based Online Voting System -- 21.1.1 Introduction -- 21.1.2 Structure of a Block in a Blockchain System -- 21.1.3 Function of Segments in a Block of the Blockchain -- 21.1.4 SHA-256 Hashing on the Blockchain -- 21.1.5 Interaction Involved in Blockchain-Based Online Voting System -- 21.1.6 Online Voting System Using Blockchain - Framework -- 21.2 Literature Review -- 21.2.1 Literature Review Outline -- 21.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model -- 21.2.1.2 Online Voting System Based on Visual Cryptography -- 21.2.1.3 Online Voting System Using Biometric Security and Steganography -- 21.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption -- 21.2.1.5 An Online Voting System Based on a Secured Blockchain -- 21.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach -- 21.2.1.7 Online Voting System Using Iris Recognition -- 21.2.1.8 Online Voting System Based on NID and SIM -- 21.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography -- 21.2.1.10 Online Voting System Using Secret Sharing-Based Authentication -- 21.2.2 Comparing the Existing Online Voting System -- References -- 22 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects 431 Abhinav Juneja, Sapna Juneja, Vikram Bali, Vishal Jain and Hemant Upadhyay -- 22.1 Introduction -- 22.2 Literature Review -- 22.3 Different Variants of Cybersecurity in Action -- 22.4 Importance of Cybersecurity in Action -- 22.5 Methods for Establishing a Strategy for Cybersecurity -- 22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity -- 22.7 Where AI Is Actually Required to Deal With Cybersecurity -- 22.8 Challenges for Cybersecurity in Current State of Practice -- 22.9 Conclusion -- References -- Index.
Artificial intelligence. Internet of things. Computer networks--Social aspects. Artificial intelligence. Computer networks--Social aspects. Internet of things.