Machine Learning Techniques and Analytics for Cloud Security

Rajdeep Chakraborty obtained his PhD in CSE from the University of Kalyani. He is currently an assistant professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia, Kolkata, India. He has several publications in reputed international journals and conferences and has authored a book on hardware cryptography. His field of interest is mainly in cryptography and computer security.Anupam Ghosh obtained his PhD in Engineering from Jadavpur University. He is currently a professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata. He has published more than 80 papers in reputed international journals and conferences. His field of interest is mainly in AI, machine learning, deep learning, image processing, soft computing, bioinformatics, IoT, data mining. Jyotsna Kumar Mandal obtained his PhD in CSE from Jadavpur University He has more than 450 publications in reputed international journals and conferences. His field of interest is mainly in coding theory, data and network security, remote sensing & GIS-based applications, data compression error corrections, information security, watermarking, steganography and document authentication, image processing, visual cryptography, MANET, wireless and mobile computing/security, unify computing, chaos theory, and applications.

• ContentsPrefacePart I: Conceptual Aspects on Cloud and Applications of Machine Learning 11 Hybrid Cloud: A New Paradigm in Cloud Computing 3Moumita Deb and Abantika Choudhury1.1 Introduction 31.2 Hybrid Cloud 51.2.1 Architecture 61.2.2 Why Hybrid Cloud is Required? 61.2.3 Business and Hybrid Cloud 71.2.4 Things to Remember When Deploying Hybrid Cloud 81.3 Comparison Among Different Hybrid Cloud Providers 91.3.1 Cloud Storage and Backup Benefits 111.3.2 Pros and Cons of Different Service Providers 111.3.2.1 AWS Outpost 121.3.2.2 Microsoft Azure Stack 121.3.2.3 Google Cloud Anthos 121.3.3 Review on Storage of the Providers 131.3.3.1 AWS Outpost Storage 131.3.3.2 Google Cloud Anthos Storage 131.3.4 Pricing 151.4 Hybrid Cloud in Education 151.5 Significance of Hybrid Cloud Post-Pandemic 151.6 Security in Hybrid Cloud 161.6.1 Role of Human Error in Cloud Security 181.6.2 Handling Security Challenges 181.7 Use of AI in Hybrid Cloud 191.8 Future Research Direction 211.9 Conclusion 22References 22xixv2 Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework 25Shillpi Mishrra2.1 Introduction 252.2 Proposed Methodology 272.3 Result 282.3.1 Description of Datasets 292.3.2 Analysis of Result 292.3.3 Validation of Results 312.3.3.1 T-Test (Statistical Validation) 312.3.3.2 Statistical Validation 332.3.4 Glycan Cloud 372.4 Conclusions and Future Work 38References 393 Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) 41Subir Hazra, Alia Nikhat Khurshid and Akriti3.1 Introduction 413.2 Related Methods 443.3 Methodology 463.3.1 Description 473.3.2 Flowchart 493.3.3 Algorithm 493.3.4 Interpretation of the Algorithm 503.3.5 Illustration 503.4 Result 513.4.1 Description of the Dataset 513.4.2 Result Analysis 513.4.3 Result Set Validation 523.5 Application in Cloud Domain 563.6 Conclusion 58References 59Part II: Cloud Security Systems Using Machine Learning Techniques 614 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology 63Soumen Santra, Partha Mukherjee and Arpan Deyasi4.1 Introduction 644.2 Home Automation System 654.2.1 Sensors 654.2.2 Protocols 664.2.3 Technologies 664.2.4 Advantages 674.2.5 Disadvantages 674.3 Literature Review 674.4 Role of Sensors and Microcontrollers in Smart Home Design 684.5 Motivation of the Project 704.6 Smart Informative and Command Accepting Interface 704.7 Data Flow Diagram 714.8 Components of Informative Interface 724.9 Results 734.9.1 Circuit Design 734.9.2 LDR Data 764.9.3 API Data 764.10 Conclusion 784.11 Future Scope 78References 785 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security 81Anirban Bhowmik, Sunil Karforma and Joydeep Dey5.1 Introduction 815.2 Literature Review 855.3 The Problem 865.4 Objectives and Contributions 865.5 Methodology 875.6 Results and Discussions 915.6.1 Statistical Analysis 935.6.2 Randomness Test of Key 945.6.3 Key Sensitivity Analysis 955.6.4 Security Analysis 965.6.5 Dataset Used on ANN 965.6.6 Comparisons 985.7 Conclusions 99References 996 An Efficient Intrusion Detection System on Various Datasets Using Machine Learning Techniques 103Debraj Chatterjee6.1 Introduction 1036.2 Motivation and Justification of the Proposed Work 1046.3 Terminology Related to IDS 1056.3.1 Network 1056.3.2 Network Traffic 1056.3.3 Intrusion 1066.3.4 Intrusion Detection System 1066.3.4.1 Various Types of IDS 1086.3.4.2 Working Methodology of IDS 1086.3.4.3 Characteristics of IDS 1096.3.4.4 Advantages of IDS 1106.3.4.5 Disadvantages of IDS 1116.3.5 Intrusion Prevention System (IPS) 1116.3.5.1 Network-Based Intrusion Prevention System (NIPS) 1116.3.5.2 Wireless Intrusion Prevention System (WIPS) 1126.3.5.3 Network Behavior Analysis (NBA) 1126.3.5.4 Host-Based Intrusion Prevention System (HIPS) 1126.3.6 Comparison of IPS With IDS/Relation Between IDS and IPS 1126.3.7 Different Methods of Evasion in Networks 1136.4 Intrusion Attacks on Cloud Environment 1146.5 Comparative Studies 1166.6 Proposed Methodology 1216.7 Result 1226.8 Conclusion and Future Scope 125References 1267 You Are Known by Your Mood: A Text-Based Sentiment Analysis for Cloud Security 129Abhijit Roy and Parthajit Roy7.1 Introduction 1297.2 Literature Review 1317.3 Essential Prerequisites 1337.3.1 Security Aspects 1337.3.2 Machine Learning Tools 1357.3.2.1 Naïve Bayes Classifier 1357.3.2.2 Artificial Neural Network 1367.4 Proposed Model 1367.5 Experimental Setup 1387.6 Results and Discussions 1397.7 Application in Cloud Security 1427.7.1 Ask an Intelligent Security Question 1427.7.2 Homomorphic Data Storage 1427.7.3 Information Diffusion 1447.8 Conclusion and Future Scope 144References 1458 The State-of-the-Art in Zero-Knowledge Authentication Proof for Cloud 149Priyanka Ghosh8.1 Introduction 1498.2 Attacks and Countermeasures 1538.2.1 Malware and Ransomware Breaches 1548.2.2 Prevention of Distributing Denial of Service 1548.2.3 Threat Detection 1548.3 Zero-Knowledge Proof 1548.4 Machine Learning for Cloud Computing 1568.4.1 Types of Learning Algorithms 1568.4.1.1 Supervised Learning 1568.4.1.2 Supervised Learning Approach 1568.4.1.3 Unsupervised Learning 1578.4.2 Application on Machine Learning for Cloud Computing 1578.4.2.1 Image Recognition 1578.4.2.2 Speech Recognition 1578.4.2.3 Medical Diagnosis 1588.4.2.4 Learning Associations 1588.4.2.5 Classification 1588.4.2.6 Prediction 1588.4.2.7 Extraction 1588.4.2.8 Regression 1588.4.2.9 Financial Services 1598.5 Zero-Knowledge Proof: Details 1598.5.1 Comparative Study 1598.5.1.1 Fiat-Shamir ZKP Protocol 1598.5.2 Diffie-Hellman Key Exchange Algorithm 1618.5.2.1 Discrete Logarithm Attack 1618.5.2.2 Man-in-the-Middle Attack 1628.5.3 ZKP Version 1 1628.5.4 ZKP Version 2 1628.5.5 Analysis 1648.5.6 Cloud Security Architecture 1668.5.7 Existing Cloud Computing Architectures 1678.5.8 Issues With Current Clouds 1678.6 Conclusion 168References 1699 A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques 171Amartya Chakraborty, Suvendu Chattaraj, Sangita Karmakar and Shillpi Mishrra9.1 Introduction 1719.2 Literature Review 1739.3 Motivation 1749.4 System Overview 1759.5 Data Description 1769.6 Data Processing 1769.7 Feature Extraction 1789.8 Learning Techniques Used 1799.8.1 Support Vector Machine 1799.8.2 k-Nearest Neighbors 1809.8.3 Decision Tree 1809.8.4 Convolutional Neural Network 1809.9 Experimental Setup 1829.10 Evaluation Metrics 1839.11 Experimental Results 1859.11.1 Observations in Comparison With State-of-the-Art 1879.12 Application in Cloud Architecture 1889.13 Conclusion 189References 19010 An Intelligent System for Securing Network From Intrusion Detection and Prevention of Phishing Attack Using Machine Learning Approaches 193Sumit Banik, Sagar Banik and Anupam Mukherjee10.1 Introduction 19310.1.1 Types of Phishing 19510.1.1.1 Spear Phishing 19510.1.1.2 Whaling 19510.1.1.3 Catphishing and Catfishing 19510.1.1.4 Clone Phishing 19610.1.1.5 Voice Phishing 19610.1.2 Techniques of Phishing 19610.1.2.1 Link Manipulation 19610.1.2.2 Filter Evasion 19610.1.2.3 Website Forgery 19610.1.2.4 Covert Redirect 19710.2 Literature Review 19710.3 Materials and Methods 19910.3.1 Dataset and Attributes 19910.3.2 Proposed Methodology 19910.3.2.1 Logistic Regression 20210.3.2.2 Naïve Bayes 20210.3.2.3 Support Vector Machine 20310.3.2.4 Voting Classification 20310.4 Result Analysis 20410.4.1 Analysis of Different Parameters for ML Models 20410.4.2 Predictive Outcome Analysis in Phishing URLs Dataset 20510.4.3 Analysis of Performance Metrics 20610.4.4 Statistical Analysis of Results 210‌0.4.4. 1 ANOVA: Two-Factor Without Replication 21010.4.4.2 ANOVA: Single Factor 21010.5 Conclusion 210References 211Part III: Cloud Security Analysis Using Machine Learning Techniques 21311 Cloud Security Using Honeypot Network and Blockchain: A Review 215Smarta Sangui * and Swarup Kr Ghosh11.1 Introduction 21511.2 Cloud Computing Overview 21611.2.1 Types of Cloud Computing Services 21611.2.1.1 Software as a Service 21611.2.1.2 Infrastructure as a Service 21811.2.1.3 Platform as a Service 21811.2.2 Deployment Models of Cloud Computing 21811.2.2.1 Public Cloud 21811.2.2.2 Private Cloud 21811.2.2.3 Community Cloud 21911.2.2.4 Hybrid Cloud 21911.2.3 Security Concerns in Cloud Computing 21911.2.3.1 Data Breaches 21911.2.3.2 Insufficient Change Control and Misconfiguration 21911.2.3.3 Lack of Strategy and Security Architecture 22011.2.3.4 Insufficient Identity, Credential, Access, and Key Management 22011.2.3.5 Account Hijacking 22011.2.3.6 Insider Threat 22011.2.3.7 Insecure Interfaces and APIs 22011.2.3.8 Weak Control Plane 22111.3 Honeypot System 22111.3.1 VM (Virtual Machine) as Honeypot in the Cloud 22111.3.2 Attack Sensing and Analyzing Framework 22211.3.3 A Fuzzy Technique Against Fingerprinting Attacks 22311.3.4 Detecting and Classifying Malicious Access 22411.3.5 A Bayesian Defense Model for Deceptive Attack 22411.3.6 Strategic Game Model for DDoS Attacks in Smart Grid 22611.4 Blockchain 22711.4.1 Blockchain-Based Encrypted Cloud Storage 22811.4.2 Cloud-Assisted EHR Sharing via Consortium Blockchain 22911.4.3 Blockchain-Secured Cloud Storage 23011.4.4 Blockchain and Edge Computing–Based Security Architecture 23011.4.5 Data Provenance Architecture in Cloud Ecosystem Using Blockchain 23111.6 Comparative Analysis 23311.7 Conclusion 233References 23412 Machine Learning–Based Security in Cloud Database—A Survey 239Utsav Vora, Jayleena Mahato, Hrishav Dasgupta, Anand Kumar and Swarup Kr Ghosh12.1 Introduction 23912.2 Security Threats and Attacks 24112.3 Dataset Description 24412.3.1 NSL-KDD Dataset 24412.3.2 UNSW-NB15 Dataset 24412.4 Machine Learning for Cloud Security 24512.4.1 Supervised Learning Techniques 24512.4.1.1 Support Vector Machine 24512.4.1.2 Artificial Neural Network 24712.4.1.3 Deep Learning 24912.4.1.4 Random Forest 25012.4.2 Unsupervised Learning Techniques 25112.4.2.1 K-Means Clustering 25212.4.2.2 Fuzzy C-Means Clustering 25312.4.2.3 Expectation-Maximization Clustering 25312.4.2.4 Cuckoo Search With Particle Swarm Optimization (PSO) 25412.4.3 Hybrid Learning Techniques 25612.4.3.1 HIDCC: Hybrid Intrusion Detection Approach in Cloud Computing 25612.4.3.2 Clustering-Based Hybrid Model in Deep Learning Framework 25712.4.3.3 K-Nearest Neighbor–Based Fuzzy C-Means Mechanism 25812.4.3.4 K-Means Clustering Using Support Vector Machine 26012.4.3.5 K-Nearest Neighbor–Based Artificial Neural Network Mechanism 26012.4.3.6 Artificial Neural Network Fused With Support Vector Machine 26112.4.3.7 Particle Swarm Optimization–Based Probabilistic Neural Network 26112.5 Comparative Analysis 26212.6 Conclusion 264References 26713 Machine Learning Adversarial Attacks: A Survey Beyond 271Chandni Magoo and Puneet Garg13.1 Introduction 27113.2 Adversarial Learning 27213.2.1 Concept 27213.3 Taxonomy of Adversarial Attacks 27313.3.1 Attacks Based on Knowledge 27313.3.1.1 Black Box Attack (Transferable Attack) 27313.3.1.2 White Box Attack 27413.3.2 Attacks Based on Goals 27513.3.2.1 Target Attacks 27513.3.2.2 Non-Target Attacks 27513.3.3 Attacks Based on Strategies 27513.3.3.1 Poisoning Attacks 27513.3.3.2 Evasion Attacks 27613.3.4 Textual-Based Attacks (NLP) 27613.3.4.1 Character Level Attacks 27613.3.4.2 Word-Level Attacks 27613.3.4.3 Sentence-Level Attacks 27613.4 Review of Adversarial Attack Methods 27613.4.1 L-bfgs 27713.4.2 Feedforward Derivation Attack (Jacobian Attack) 27713.4.3 Fast Gradient Sign Method 27813.4.4 Methods of Different Text-Based Adversarial Attacks 27813.4.5 Adversarial Attacks Methods Based on Language Models 28413.4.6 Adversarial Attacks on Recommender Systems 28413.4.6.1 Random Attack 28413.4.6.2 Average Attack 28613.4.6.3 Bandwagon Attack 28613.4.6.4 Reverse Bandwagon Attack 28613.5 Adversarial Attacks on Cloud-Based Platforms 28713.6 Conclusion 288References 28814 Protocols for Cloud Security 293Weijing You and Bo Chen14.1 Introduction 29314.2 System and Adversarial Model 29514.2.1 System Model 29514.2.2 Adversarial Model 29514.3 Protocols for Data Protection in Secure Cloud Computing 29614.3.1 Homomorphic Encryption 29714.3.2 Searchable Encryption 29814.3.3 Attribute-Based Encryption 29914.3.4 Secure Multi-Party Computation 30014.4 Protocols for Data Protection in Secure Cloud Storage 30114.4.1 Proofs of Encryption 30114.4.2 Secure Message-Locked Encryption 30314.4.3 Proofs of Storage 30314.4.4 Proofs of Ownership 30514.4.5 Proofs of Reliability 30614.5 Protocols for Secure Cloud Systems 30914.6 Protocols for Cloud Security in the Future 30914.7 Conclusion 310References 311Part IV: Case Studies Focused on Cloud Security 31315 A Study on Google Cloud Platform (GCP) and Its Security 315Agniswar Roy, Abhik Banerjee and Navneet Bhardwaj15.1 Introduction 31515.1.1 Google Cloud Platform Current Market Holding 31615.1.1.1 The Forrester Wave 31715.1.1.2 Gartner Magic Quadrant 31715.1.2 Google Cloud Platform Work Distribution 31715.1.2.1 SaaS 31815.1.2.2 PaaS 31815.1.2.3 IaaS 31815.1.2.4 On-Premise 31815.2 Google Cloud Platform’s Security Features Basic Overview 31815.2.1 Physical Premises Security 31915.2.2 Hardware Security 31915.2.3 Inter-Service Security 31915.2.4 Data Security 32015.2.5 Internet Security 32015.2.6 In-Software Security 32015.2.7 End User Access Security 32115.3 Google Cloud Platform’s Architecture 32115.3.1 Geographic Zone 32115.3.2 Resource Management 32215.3.2.1 Iam 32215.3.2.2 Roles 32315.3.2.3 Billing 32315.4 Key Security Features 32415.4.1 Iap 32415.4.2 Compliance 32515.4.3 Policy Analyzer 32615.4.4 Security Command Center 32615.4.4.1 Standard Tier 32615.4.4.2 Premium Tier 32615.4.5 Data Loss Protection 32915.4.6 Key Management 32915.4.7 Secret Manager 33015.4.8 Monitoring 33015.5 Key Application Features 33015.5.1 Stackdriver (Currently Operations) 33015.5.1.1 Profiler 33015.5.1.2 Cloud Debugger 33015.5.1.3 Trace 33115.5.2 Network 33115.5.3 Virtual Machine Specifications 33215.5.4 Preemptible VMs 33215.6 Computation in Google Cloud Platform 33215.6.1 Compute Engine 33215.6.2 App Engine 33315.6.3 Container Engine 33315.6.4 Cloud Functions 33315.7 Storage in Google Cloud Platform 33315.8 Network in Google Cloud Platform 33415.9 Data in Google Cloud Platform 33415.10 Machine Learning in Google Cloud Platform 33515.11 Conclusion 335References 33716 Case Study of Azure and Azure Security Practices 339Navneet Bhardwaj, Abhik Banerjee and Agniswar Roy16.1 Introduction 33916.1.1 Azure Current Market Holding 34016.1.2 The Forrester Wave 34016.1.3 Gartner Magic Quadrant 34016.2 Microsoft Azure—The Security Infrastructure 34116.2.1 Azure Security Features and Tools 34116.2.2 Network Security 34216.3 Data Encryption 34216.3.1 Data Encryption at Rest 34216.3.2 Data Encryption at Transit 34216.3.3 Asset and Inventory Management 34316.3.4 Azure Marketplace 34316.4 Azure Cloud Security Architecture 34416.4.1 Working 34416.4.2 Design Principles 34416.4.2.1 Alignment of Security Policies 34416.4.2.2 Building a Comprehensive Strategy 34516.4.2.3 Simplicity Driven 34516.4.2.4 Leveraging Native Controls 34516.4.2.5 Identification-Based Authentication 34516.4.2.6 Accountability 34516.4.2.7 Embracing Automation 34516.4.2.8 Stress on Information Protection 34516.4.2.9 Continuous Evaluation 34616.4.2.10 Skilled Workforce 34616.5 Azure Architecture 34616.5.1 Components 34616.5.1.1 Azure Api Gateway 34616.5.1.2 Azure Functions 34616.5.2 Services 34716.5.2.1 Azure Virtual Machine 34716.5.2.2 Blob Storage 34716.5.2.3 Azure Virtual Network 34816.5.2.4 Content Delivery Network 34816.5.2.5 Azure SQL Database 34916.6 Features of Azure 35016.6.1 Key Features 35016.6.1.1 Data Resiliency 35016.6.1.2 Data Security 35016.6.1.3 BCDR Integration 35016.6.1.4 Storage Management 35116.6.1.5 Single Pane View 35116.7 Common Azure Security Features 35116.7.1 Security Center 35116.7.2 Key Vault 35116.7.3 Azure Active Directory 35216.7.3.1 Application Management 35216.7.3.2 Conditional Access 35216.7.3.3 Device Identity Management 352​16.7.3. 4 Identity Protection 35316.7.3.5 Azure Sentinel 35316.7.3.6 Privileged Identity Management 35416.7.3.7 Multifactor Authentication 35416.7.3.8 Single Sign On 35416.8 Conclusion 355References 35517 Nutanix Hybrid Cloud From Security Perspective 357Abhik Banerjee, Agniswar Roy, Amar Kalvikatte and Navneet Bhardwaj17.1 Introduction 35717.2 Growth of Nutanix 35817.2.1 Gartner Magic Quadrant 35817.2.2 The Forrester Wave 35817.2.3 Consumer Acquisition 35917.2.4 Revenue 35917.3 Introductory Concepts 36117.3.1 Plane Concepts 36117.3.1.1 Control Plane 36117.3.1.2 Data Plane 36117.3.2 Security Technical Implementation Guides 36217.3.3 SaltStack and SCMA 36217.4 Nutanix Hybrid Cloud 36217.4.1 Prism 36217.4.1.1 Prism Element 36317.4.1.2 Prism Central 36417.4.2 Acropolis 36517.4.2.1 Distributed Storage Fabric 36517.4.2.2 Ahv 36717.5 Reinforcing AHV and Controller VM 36717.6 Disaster Management and Recovery 36817.6.1 Protection Domains and Consistent Groups 36817.6.2 Nutanix DSF Replication of OpLog 36917.6.3 DSF Snapshots and VmQueisced Snapshot Service 37017.6.4 Nutanix Cerebro 37017.7 Security and Policy Management on Nutanix Hybrid Cloud 37117.7.1 Authentication on Nutanix 37217.7.2 Nutanix Data Encryption 37217.7.3 Security Policy Management 37317.7.3.1 Enforcing a Policy 37417.7.3.2 Priority of a Policy 37417.7.3.3 Automated Enforcement 37417.8 Network Security and Log Management 37417.8.1 Segmented and Unsegmented Network 37517.9 Conclusion 376References 376Part V: Policy Aspects 37918 A Data Science Approach Based on User Interactions to Generate Access Control Policies for Large Collections of Documents 381Jedidiah Yanez-Sierra, Arturo Diaz-Perez and Victor Sosa-Sosa18.1 Introduction 38118.2 Related Work 38318.3 Network Science Theory 38418.4 Approach to Spread Policies Using Networks Science 38718.4.1 Finding the Most Relevant Spreaders 38818.4.1.1 Weighting Users 38918.4.1.2 Selecting the Top � Spreaders 39018.4.2 Assign and Spread the Access Control Policies 39018.4.2.1 Access Control Policies 39118.4.2.2 Horizontal Spreading 39118.4.2.3 Vertical Spreading (Bottom-Up) 39218.4.2.4 Policies Refinement 39518.4.3 Structural Complexity Analysis of CP-ABE Policies 39518.4.3.1 Assessing the WSC for ABE Policies 39618.4.3.2 Assessing the Policies Generated in the Spreading Process 39718.4.4 Effectiveness Analysis 39818.4.4.1 Evaluation Metrics 39918.4.4.2 Adjusting the Interaction Graph to Assess Policy Effectiveness 40018.4.4.3 Method to Complement the User Interactions (Synthetic Edges Generation) 40018.4.5 Measuring Policy Effectiveness in the User Interaction Graph 40318.4.5.1 Simple Node-Based Strategy 40318.4.5.2 Weighted Node-Based Strategy 40418.5 Evaluation 40518.5.1 Dataset Description 40518.5.2 Results of the Complexity Evaluation 40618.5.3 Effectiveness Results From the Real Edges 40718.5.4 Effectiveness Results Using Real and Synthetic Edges 40818.5.4.1 Results of the Effectiveness Metrics for the Enhanced G + Graph 41018.6 Conclusions 413References 41419 AI, ML, & Robotics in iSchools: An Academic Analysis for an Intelligent Societal Systems 417P. K. Paul19.1 Introduction 41719.2 Objective 41919.3 Methodology 42019.3.1 iSchools, Technologies, and Artificial Intelligence, ML, and Robotics 42019.4 Artificial Intelligence, ML, and Robotics: An Overview 42719.5 Artificial Intelligence, ML, and Robotics as an Academic Program: A Case on iSchools—North American Region 42819.6 Suggestions 43119.7 Motivation and Future Works 43519.8 Conclusion 435References 436Index 439