Preface xix Acknowledgment xxiii 1 Machine Learning-Based Data Analysis 1 M. Deepika and K. Kalaiselvi 1.1 Introduction 1 1.2 Machine Learning for the Internet of Things Using Data Analysis 4 1.2.1 Computing Framework 6 1.2.

2 Fog Computing 6 1.2.3 Edge Computing 6 1.2.4 Cloud Computing 7 1.2.5 Distributed Computing 7 1.3 Machine Learning Applied to Data Analysis 7 1.

3.1 Supervised Learning Systems 8 1.3.2 Decision Trees 9 1.3.3 Decision Tree Types 9 1.3.4 Unsupervised Machine Learning 10 1.

3.5 Association Rule Learning 10 1.3.6 Reinforcement Learning 10 1.4 Practical Issues in Machine Learning 11 1.5 Data Acquisition 12 1.6 Understanding the Data Formats Used in Data Analysis Applications 13 1.7 Data Cleaning 14 1.

8 Data Visualization 15 1.9 Understanding the Data Analysis Problem-Solving Approach 15 1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 16 1.11 Statistical Data Analysis Techniques 17 1.11.1 Hypothesis Testing 18 1.11.2 Regression Analysis 18 1.

12 Text Analysis and Visual and Audio Analysis 18 1.13 Mathematical and Parallel Techniques for Data Analysis 19 1.13.1 Using Map-Reduce 20 1.13.2 Leaning Analysis 20 1.13.3 Market Basket Analysis 21 1.

14 Conclusion 21 References 22 2 Machine Learning for Cyber-Immune IoT Applications 25 Suchismita Sahoo and Sushree Sangita Sahoo 2.1 Introduction 25 2.2 Some Associated Impactful Terms 27 2.2.1 IoT 27 2.2.2 IoT Device 28 2.2.

3 IoT Service 29 2.2.4 Internet Security 29 2.2.5 Data Security 30 2.2.6 Cyberthreats 31 2.2.

7 Cyber Attack 31 2.2.8 Malware 32 2.2.9 Phishing 32 2.2.10 Ransomware 33 2.2.

11 Spear-Phishing 33 2.2.12 Spyware 34 2.2.13 Cybercrime 34 2.2.14 IoT Cyber Security 35 2.2.

15 IP Address 36 2.3 Cloud Rationality Representation 36 2.3.1 Cloud 36 2.3.2 Cloud Data 37 2.3.3 Cloud Security 38 2.

3.4 Cloud Computing 38 2.4 Integration of IoT With Cloud 40 2.5 The Concepts That Rules Over 41 2.5.1 Artificial Intelligent 41 2.5.2 Overview of Machine Learning 41 2.

5.2.1 Supervised Learning 41 2.5.2.2 Unsupervised Learning 42 2.5.3 Applications of Machine Learning in Cyber Security 43 2.

5.4 Applications of Machine Learning in Cybercrime 43 2.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 43 2.5.6 Distributed Denial-of-Service 44 2.6 Related Work 45 2.7 Methodology 46 2.

8 Discussions and Implications 48 2.9 Conclusion 49 References 49 3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 53 Rakhi Akhare, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh 3.1 Introduction 53 3.2 Related Work 55 3.3 Predictive Data Analytics in Retail 56 3.3.1 ML for Predictive Data Analytics 58 3.3.

2 Use Cases 59 3.3.3 Limitations and Challenges 61 3.4 Proposed Model 61 3.4.1 Case Study 63 3.5 Conclusion and Future Scope 68 References 69 4 Emerging Cloud Computing Trends for Business Transformation 71 Prasanta Kumar Mahapatra, Alok Ranjan Tripathy and Alakananda Tripathy 4.1 Introduction 71 4.

1.1 Computing Definition Cloud 72 4.1.2 Advantages of Cloud Computing Over On-Premises IT Operation 73 4.1.3 Limitations of Cloud Computing 74 4.2 History of Cloud Computing 74 4.3 Core Attributes of Cloud Computing 75 4.

4 Cloud Computing Models 77 4.4.1 Cloud Deployment Model 77 4.4.2 Cloud Service Model 79 4.5 Core Components of Cloud Computing Architecture: Hardware and Software 83 4.6 Factors Need to Consider for Cloud Adoption 84 4.6.

1 Evaluating Cloud Infrastructure 84 4.6.2 Evaluating Cloud Provider 85 4.6.3 Evaluating Cloud Security 86 4.6.4 Evaluating Cloud Services 86 4.6.

5 Evaluating Cloud Service Level Agreements (SLA) 87 4.6.6 Limitations to Cloud Adoption 87 4.7 Transforming Business Through Cloud 88 4.8 Key Emerging Trends in Cloud Computing 89 4.8.1 Technology Trends 90 4.8.

2 Business Models 92 4.8.3 Product Transformation 92 4.8.4 Customer Engagement 92 4.8.5 Employee Empowerment 93 4.8.

6 Data Management and Assurance 93 4.8.7 Digitalization 93 4.8.8 Building Intelligence Cloud System 93 4.8.9 Creating Hyper-Converged Infrastructure 94 4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 94 4.

10 Conclusion 95 References 96 5 Security of Sensitive Data in Cloud Computing 99 Kirti Wanjale, Monika Mangla and Paritosh Marathe 5.1 Introduction 100 5.1.1 Characteristics of Cloud Computing 100 5.1.2 Deployment Models for Cloud Services 101 5.1.3 Types of Cloud Delivery Models 102 5.

2 Data in Cloud 102 5.2.1 Data Life Cycle 103 5.3 Security Challenges in Cloud Computing for Data 105 5.3.1 Security Challenges Related to Data at Rest 106 5.3.2 Security Challenges Related to Data in Use 107 5.

3.3 Security Challenges Related to Data in Transit 107 5.4 Cross-Cutting Issues Related to Network in Cloud 108 5.5 Protection of Data 109 5.6 Tighter IAM Controls 114 5.7 Conclusion and Future Scope 117 References 117 6 Cloud Cryptography for Cloud Data Analytics in IoT 119 N. Jayashri and K. Kalaiselvi 6.

1 Introduction 120 6.2 Cloud Computing Software Security Fundamentals 120 6.3 Security Management 122 6.4 Cryptography Algorithms 123 6.4.1 Types of Cryptography 123 6.5 Secure Communications 127 6.6 Identity Management and Access Control 133 6.

7 Autonomic Security 137 6.8 Conclusion 139 References 139 7 Issues and Challenges of Classical Cryptography in Cloud Computing 143 Amrutanshu Panigrahi, Ajit Kumar Nayak and Rourab Paul 7.1 Introduction 144 7.1.1 Problem Statement and Motivation 145 7.1.2 Contribution 146 7.2 Cryptography 146 7.

2.1 Cryptography Classification 147 7.2.1.1 Classical Cryptography 147 7.2.1.2 Homomorphic Encryption 149 7.

3 Security in Cloud Computing 150 7.3.1 The Need for Security in Cloud Computing 151 7.3.2 Challenges in Cloud Computing Security 152 7.3.3 Benefits of Cloud Computing Security 153 7.3.

4 Literature Survey 154 7.4 Classical Cryptography for Cloud Computing 157 7.4.1 RSA 157 7.4.2 AES 157 7.4.3 DES 158 7.

4.4 Blowfish 158 7.5 Homomorphic Cryptosystem 158 7.5.1 Paillier Cryptosystem 159 7.5.1.1 Additive Homomorphic Property 159 7.

5.2 RSA Homomorphic Cryptosystem 160 7.5.2.1 Multiplicative Homomorphic Property 160 7.6 Implementation 160 7.7 Conclusion and Future Scope 162 References 162 8 Cloud-Based Data Analytics for Monitoring Smart Environments 167 D. Karthika 8.

1 Introduction 167 8.2 Environmental Monitoring for Smart Buildings 169 8.2.1 Smart Environments 169 8.3 Smart Health 171 8.3.1 Description of Solutions in General 171 8.3.

2 Detection of Distress 172 8.3.3 Green Protection 173 8.3.4 Medical Preventive/Help 174 8.4 Digital Network 5G and Broadband Networks 174 8.4.1 IoT-Based Smart Grid Technologies 174 8.

5 Emergent Smart Cities Communication Networks 175 8.5.1 RFID Technologies 177 8.5.2 Identifier Schemes 177 8.6 Smart City IoT Platforms Analysis System 177 8.7 Smart Management of Car Parking in Smart Cities 178 8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach 178 8.

9 Virtual Integrated Storage System 179 8.10 Convolutional Neural Network (CNN) 181 8.10.1 IEEE 802.15.4 182 8.10.2 BLE 182 8.

10.3 ITU-T G.9959 (Z-Wave) 183 8.10.4 NFC 183 8.10.5 LoRaWAN 184 8.10.

6 Sigfox 184 8.10.7 NB-IoT 184 8.10.8 PLC 184 8.10.9 MS/TP 184 8.11 Challenges and Issues 185 8.

11.1 Interoperability and Standardization 185 8.11.2 Customization and Adaptation 186 8.11.3 Entity Identification and Virtualization 187 8.11.4 Big Data Issue in Smart Environments 187 8.

12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things 188 8.13 Case Study 189 8.14 Conclusion 191 References 191 9 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform 195 Nidhi Rajak and Ranjit Rajak 9.1 Introduction 195 9.2 Workflow Model 197 9.3 System Computing Model 198 9.4 Major Objective of Scheduling 198 9.5 Task Computational Attributes for Scheduling 198 9.

6 Performance Metrics 200 9.7 Heuristic Task Scheduling Algorithms 201 9.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 202 9.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm 208 9.7.3 As Late As Possible (ALAP) Algorithm 213 9.

7.4 Performance Effective Task Scheduling (PETS) Algorithm 217 9.8 Performance Analysis and Results 220 9.9 Conclusion 224 References 224 10 Smart Environment Monitoring Models Using Cl.