Contents Preface Part I: Conceptual Aspects on Cloud and Applications of Machine Learning 1 1 Hybrid Cloud: A New Paradigm in Cloud Computing 3 Moumita Deb and Abantika Choudhury 1.1 Introduction 3 1.2 Hybrid Cloud 5 1.2.1 Architecture 6 1.2.2 Why Hybrid Cloud is Required? 6 1.2.3 Business and Hybrid Cloud 7 1.2.4 Things to Remember When Deploying Hybrid Cloud 8 1.3 Comparison Among Different Hybrid Cloud Providers 9 1.3.1 Cloud Storage and Backup Benefits 11 1.3.2 Pros and Cons of Different Service Providers 11 1.3.2.1 AWS Outpost 12 1.3.2.2 Microsoft Azure Stack 12 1.3.2.3 Google Cloud Anthos 12 1.3.3 Review on Storage of the Providers 13 1.3.3.1 AWS Outpost Storage 13 1.3.3.2 Google Cloud Anthos Storage 13 1.3.4 Pricing 15 1.4 Hybrid Cloud in Education 15 1.5 Significance of Hybrid Cloud Post-Pandemic 15 1.6 Security in Hybrid Cloud 16 1.6.1 Role of Human Error in Cloud Security 18 1.6.2 Handling Security Challenges 18 1.7 Use of AI in Hybrid Cloud 19 1.8 Future Research Direction 21 1.9 Conclusion 22 References 22 xix v 2 Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework 25 Shillpi Mishrra 2.1 Introduction 25 2.2 Proposed Methodology 27 2.3 Result 28 2.3.1 Description of Datasets 29 2.3.2 Analysis of Result 29 2.3.3 Validation of Results 31 2.3.3.1 T-Test (Statistical Validation) 31 2.3.3.2 Statistical Validation 33 2.3.4 Glycan Cloud 37 2.4 Conclusions and Future Work 38 References 39 3 Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) 41 Subir Hazra, Alia Nikhat Khurshid and Akriti 3.1 Introduction 41 3.2 Related Methods 44 3.3 Methodology 46 3.3.1 Description 47 3.3.2 Flowchart 49 3.3.3 Algorithm 49 3.3.4 Interpretation of the Algorithm 50 3.3.5 Illustration 50 3.4 Result 51 3.4.1 Description of the Dataset 51 3.4.2 Result Analysis 51 3.4.3 Result Set Validation 52 3.5 Application in Cloud Domain 56 3.6 Conclusion 58 References 59 Part II: Cloud Security Systems Using Machine Learning Techniques 61 4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology 63 Soumen Santra, Partha Mukherjee and Arpan Deyasi 4.1 Introduction 64 4.2 Home Automation System 65 4.2.1 Sensors 65 4.2.2 Protocols 66 4.2.3 Technologies 66 4.2.4 Advantages 67 4.2.5 Disadvantages 67 4.3 Literature Review 67 4.4 Role of Sensors and Microcontrollers in Smart Home Design 68 4.5 Motivation of the Project 70 4.6 Smart Informative and Command Accepting Interface 70 4.7 Data Flow Diagram 71 4.8 Components of Informative Interface 72 4.9 Results 73 4.9.1 Circuit Design 73 4.9.2 LDR Data 76 4.9.3 API Data 76 4.10 Conclusion 78 4.11 Future Scope 78 References 78 5 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security 81 Anirban Bhowmik, Sunil Karforma and Joydeep Dey 5.1 Introduction 81 5.2 Literature Review 85 5.3 The Problem 86 5.4 Objectives and Contributions 86 5.5 Methodology 87 5.6 Results and Discussions 91 5.6.1 Statistical Analysis 93 5.6.2 Randomness Test of Key 94 5.6.3 Key Sensitivity Analysis 95 5.6.4 Security Analysis 96 5.6.5 Dataset Used on ANN 96 5.6.6 Comparisons 98 5.7 Conclusions 99 References 99 6 An Efficient Intrusion Detection System on Various Datasets Using Machine Learning Techniques 103 Debraj Chatterjee 6.1 Introduction 103 6.2 Motivation and Justification of the Proposed Work 104 6.3 Terminology Related to IDS 105 6.3.1 Network 105 6.3.2 Network Traffic 105 6.3.3 Intrusion 106 6.3.4 Intrusion Detection System 106 6.3.4.1 Various Types of IDS 108 6.3.4.2 Working Methodology of IDS 108 6.3.4.3 Characteristics of IDS 109 6.3.4.4 Advantages of IDS 110 6.3.4.5 Disadvantages of IDS 111 6.3.5 Intrusion Prevention System (IPS) 111 6.3.5.1 Network-Based Intrusion Prevention System (NIPS) 111 6.3.5.2 Wireless Intrusion Prevention System (WIPS) 112 6.3.5.3 Network Behavior Analysis (NBA) 112 6.3.5.4 Host-Based Intrusion Prevention System (HIPS) 112 6.3.6 Comparison of IPS With IDS/Relation Between IDS and IPS 112 6.3.7 Different Methods of Evasion in Networks 113 6.4 Intrusion Attacks on Cloud Environment 114 6.5 Comparative Studies 116 6.6 Proposed Methodology 121 6.7 Result 122 6.8 Conclusion and Future Scope 125 References 126 7 You Are Known by Your Mood: A Text-Based Sentiment Analysis for Cloud Security 129 Abhijit Roy and Parthajit Roy 7.1 Introduction 129 7.2 Literature Review 131 7.3 Essential Prerequisites 133 7.3.1 Security Aspects 133 7.3.2 Machine Learning Tools 135 7.3.2.1 Naïve Bayes Classifier 135 7.3.2.2 Artificial Neural Network 136 7.4 Proposed Model 136 7.5 Experimental Setup 138 7.6 Results and Discussions 139 7.7 Application in Cloud Security 142 7.7.1 Ask an Intelligent Security Question 142 7.7.2 Homomorphic Data Storage 142 7.7.3 Information Diffusion 144 7.8 Conclusion and Future Scope 144 References 145 8 The State-of-the-Art in Zero-Knowledge Authentication Proof for Cloud 149 Priyanka Ghosh 8.1 Introduction 149 8.2 Attacks and Countermeasures 153 8.2.1 Malware and Ransomware Breaches 154 8.2.2 Prevention of Distributing Denial of Service 154 8.2.3 Threat Detection 154 8.3 Zero-Knowledge Proof 154 8.4 Machine Learning for Cloud Computing 156 8.4.1 Types of Learning Algorithms 156 8.4.1.1 Supervised Learning 156 8.4.1.2 Supervised Learning Approach 156 8.4.1.3 Unsupervised Learning 157 8.4.2 Application on Machine Learning for Cloud Computing 157 8.4.2.1 Image Recognition 157 8.4.2.2 Speech Recognition 157 8.4.2.3 Medical Diagnosis 158 8.4.2.4 Learning Associations 158 8.4.2.5 Classification 158 8.4.2.6 Prediction 158 8.4.2.7 Extraction 158 8.4.2.8 Regression 158 8.4.2.9 Financial Services 159 8.5 Zero-Knowledge Proof: Details 159 8.5.1 Comparative Study 159 8.5.1.1 Fiat-Shamir ZKP Protocol 159 8.5.2 Diffie-Hellman Key Exchange Algorithm 161 8.5.2.1 Discrete Logarithm Attack 161 8.5.2.2 Man-in-the-Middle Attack 162 8.5.3 ZKP Version 1 162 8.5.4 ZKP Version 2 162 8.5.5 Analysis 164 8.5.6 Cloud Security Architecture 166 8.5.7 Existing Cloud Computing Architectures 167 8.5.8 Issues With Current Clouds 167 8.6 Conclusion 168 References 169 9 A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques 171 Amartya Chakraborty, Suvendu Chattaraj, Sangita Karmakar and Shillpi Mishrra 9.1 Introduction 171 9.2 Literature Review 173 9.3 Motivation 174 9.4 System Overview 175 9.5 Data Description 176 9.6 Data Processing 176 9.7 Feature Extraction 178 9.8 Learning Techniques Used 179 9.8.1 Support Vector Machine 179 9.8.2 k-Nearest Neighbors 180 9.8.3 Decision Tree 180 9.8.4 Convolutional Neural Network 180 9.9 Experimental Setup 182 9.10 Evaluation Metrics 183 9.11 Experimental Results 185 9.11.1 Observations in Comparison With State-of-the-Art 187 9.12 Application in Cloud Architecture 188 9.13 Conclusion 189 References 190 10 An Intelligent System for Securing Network From Intrusion Detection and Prevention of Phishing Attack Using Machine Learning Approaches 193 Sumit Banik, Sagar Banik and Anupam Mukherjee 10.1 Introduction 193 10.1.1 Types of Phishing 195 10.1.1.1 Spear Phishing 195 10.1.1.2 Whaling 195 10.1.1.3 Catphishing and Catfishing 195 10.1.1.4 Clone Phishing 196 10.1.1.5 Voice Phishing 196 10.1.2 Techniques of Phishing 196 10.1.2.1 Link Manipulation 196 10.1.2.2 Filter Evasion 196 10.1.2.3 Website Forgery 196 10.1.2.4 Covert Redirect 197 10.2 Literature Review 197 10.3 Materials and Methods 199 10.3.1 Dataset and Attributes 199 10.3.2 Proposed Methodology 199 10.3.2.1 Logistic Regression 202 10.3.2.2 Naïve Bayes 202 10.3.2.3 Support Vector Machine 203 10.3.2.4 Voting Classification 203 10.4 Result Analysis 204 10.4.1 Analysis of Different Parameters for ML Models 204 10.4.2 Predictive Outcome Analysis in Phishing URLs Dataset 205 10.4.3 Analysis of Performance Metrics 206 10.4.4 Statistical Analysis of Results 210 ‌0.4.4. 1 ANOVA: Two-Factor Without Replication 210 10.4.4.2 ANOVA: Single Factor 210 10.5 Conclusion 210 References 211 Part III: Cloud Security Analysis Using Machine Learning Techniques 213 11 Cloud Security Using Honeypot Network and Blockchain: A Review 215 Smarta Sangui * and Swarup Kr Ghosh 11.1 Introduction 215 11.2 Cloud Computing Overview 216 11.2.1 Types of Cloud Computing Services 216 11.2.1.1 Software as a Service 216 11.2.1.2 Infrastructure as a Service 218 11.2.1.3 Platform as a Service 218 11.2.2 Deployment Models of Cloud Computing 218 11.2.2.1 Public Cloud 218 11.2.2.2 Private Cloud 218 11.2.2.3 Community Cloud 219 11.2.2.4 Hybrid Cloud 219 11.2.3 Security Concerns in Cloud Computing 219 11.2.3.1 Data Breaches 219 11.2.3.2 Insufficient Change Control and Misconfiguration 219 11.2.3.3 Lack of Strategy and Security Architecture 220 11.2.3.4 Insufficient Identity, Credential, Access, and Key Management 220 11.2.3.5 Account Hijacking 220 11.2.3.6 Insider Threat 220 11.2.3.7 Insecure Interfaces and APIs 220 11.2.3.8 Weak Control Plane 221 11.3 Honeypot System 221 11.3.1 VM (Virtual Machine) as Honeypot in the Cloud 221 11.3.2 Attack Sensing and Analyzing Framework 222 11.3.3 A Fuzzy Technique Against Fingerprinting Attacks 223 11.3.4 Detecting and Classifying Malicious Access 224 11.3.5 A Bayesian Defense Model for Deceptive Attack 224 11.3.6 Strategic Game Model for DDoS Attacks in Smart Grid 226 11.4 Blockchain 227 11.4.1 Blockchain-Based Encrypted Cloud Storage 228 11.4.2 Cloud-Assisted EHR Sharing via Consortium Blockchain 229 11.4.3 Blockchain-Secured Cloud Storage 230 11.4.4 Blockchain and Edge Computing–Based Security Architecture 230 11.4.5 Data Provenance Architecture in Cloud Ecosystem Using Blockchain 231 11.6 Comparative Analysis 233 11.7 Conclusion 233 References 234 12 Machine Learning–Based Security in Cloud Database—A Survey 239 Utsav Vora, Jayleena Mahato, Hrishav Dasgupta, Anand Kumar and Swarup Kr Ghosh 12.1 Introduction 239 12.2 Security Threats and Attacks 241 12.3 Dataset Description 244 12.3.1 NSL-KDD Dataset 244 12.3.2 UNSW-NB15 Dataset 244 12.4 Machine Learning for Cloud Security 245 12.4.1 Supervised Learning Techniques 245 12.4.1.1 Support Vector Machine 245 12.4.1.2 Artificial Neural Network 247 12.4.1.3 Deep Learning 249 12.4.1.4 Random Forest 250 12.4.2 Unsupervised Learning Techniques 251 12.4.2.1 K-Means Clustering 252 12.4.2.2 Fuzzy C-Means Clustering 253 12.4.2.3 Expectation-Maximization Clustering 253 12.4.2.4 Cuckoo Search With Particle Swarm Optimization (PSO) 254 12.4.3 Hybrid Learning Techniques 256 12.4.3.1 HIDCC: Hybrid Intrusion Detection Approach in Cloud Computing 256 12.4.3.2 Clustering-Based Hybrid Model in Deep Learning Framework 257 12.4.3.3 K-Nearest Neighbor–Based Fuzzy C-Means Mechanism 258 12.4.3.4 K-Means Clustering Using Support Vector Machine 260 12.4.3.5 K-Nearest Neighbor–Based Artificial Neural Network Mechanism 260 12.4.3.6 Artificial Neural Network Fused With Support Vector Machine 261 12.4.3.7 Particle Swarm Optimization–Based Probabilistic Neural Network 261 12.5 Comparative Analysis 262 12.6 Conclusion 264 References 267 13 Machine Learning Adversarial Attacks: A Survey Beyond 271 Chandni Magoo and Puneet Garg 13.1 Introduction 271 13.2 Adversarial Learning 272 13.2.1 Concept 272 13.3 Taxonomy of Adversarial Attacks 273 13.3.1 Attacks Based on Knowledge 273 13.3.1.1 Black Box Attack (Transferable Attack) 273 13.3.1.2 White Box Attack 274 13.3.2 Attacks Based on Goals 275 13.3.2.1 Target Attacks 275 13.3.2.2 Non-Target Attacks 275 13.3.3 Attacks Based on Strategies 275 13.3.3.1 Poisoning Attacks 275 13.3.3.2 Evasion Attacks 276 13.3.4 Textual-Based Attacks (NLP) 276 13.3.4.1 Character Level Attacks 276 13.3.4.2 Word-Level Attacks 276 13.3.4.3 Sentence-Level Attacks 276 13.4 Review of Adversarial Attack Methods 276 13.4.1 L-bfgs 277 13.4.2 Feedforward Derivation Attack (Jacobian Attack) 277 13.4.3 Fast Gradient Sign Method 278 13.4.4 Methods of Different Text-Based Adversarial Attacks 278 13.4.5 Adversarial Attacks Methods Based on Language Models 284 13.4.6 Adversarial Attacks on Recommender Systems 284 13.4.6.1 Random Attack 284 13.4.6.2 Average Attack 286 13.4.6.3 Bandwagon Attack 286 13.4.6.4 Reverse Bandwagon Attack 286 13.5 Adversarial Attacks on Cloud-Based Platforms 287 13.6 Conclusion 288 References 288 14 Protocols for Cloud Security 293 Weijing You and Bo Chen 14.1 Introduction 293 14.2 System and Adversarial Model 295 14.2.1 System Model 295 14.2.2 Adversarial Model 295 14.3 Protocols for Data Protection in Secure Cloud Computing 296 14.3.1 Homomorphic Encryption 297 14.3.2 Searchable Encryption 298 14.3.3 Attribute-Based Encryption 299 14.3.4 Secure Multi-Party Computation 300 14.4 Protocols for Data Protection in Secure Cloud Storage 301 14.4.1 Proofs of Encryption 301 14.4.2 Secure Message-Locked Encryption 303 14.4.3 Proofs of Storage 303 14.4.4 Proofs of Ownership 305 14.4.5 Proofs of Reliability 306 14.5 Protocols for Secure Cloud Systems 309 14.6 Protocols for Cloud Security in the Future 309 14.7 Conclusion 310 References 311 Part IV: Case Studies Focused on Cloud Security 313 15 A Study on Google Cloud Platform (GCP) and Its Security 315 Agniswar Roy, Abhik Banerjee and Navneet Bhardwaj 15.1 Introduction 315 15.1.1 Google Cloud Platform Current Market Holding 316 15.1.1.1 The Forrester Wave 317 15.1.1.2 Gartner Magic Quadrant 317 15.1.2 Google Cloud Platform Work Distribution 317 15.1.2.1 SaaS 318 15.1.2.2 PaaS 318 15.1.2.3 IaaS 318 15.1.2.4 On-Premise 318 15.2 Google Cloud Platform’s Security Features Basic Overview 318 15.2.1 Physical Premises Security 319 15.2.2 Hardware Security 319 15.2.3 Inter-Service Security 319 15.2.4 Data Security 320 15.2.5 Internet Security 320 15.2.6 In-Software Security 320 15.2.7 End User Access Security 321 15.3 Google Cloud Platform’s Architecture 321 15.3.1 Geographic Zone 321 15.3.2 Resource Management 322 15.3.2.1 Iam 322 15.3.2.2 Roles 323 15.3.2.3 Billing 323 15.4 Key Security Features 324 15.4.1 Iap 324 15.4.2 Compliance 325 15.4.3 Policy Analyzer 326 15.4.4 Security Command Center 326 15.4.4.1 Standard Tier 326 15.4.4.2 Premium Tier 326 15.4.5 Data Loss Protection 329 15.4.6 Key Management 329 15.4.7 Secret Manager 330 15.4.8 Monitoring 330 15.5 Key Application Features 330 15.5.1 Stackdriver (Currently Operations) 330 15.5.1.1 Profiler 330 15.5.1.2 Cloud Debugger 330 15.5.1.3 Trace 331 15.5.2 Network 331 15.5.3 Virtual Machine Specifications 332 15.5.4 Preemptible VMs 332 15.6 Computation in Google Cloud Platform 332 15.6.1 Compute Engine 332 15.6.2 App Engine 333 15.6.3 Container Engine 333 15.6.4 Cloud Functions 333 15.7 Storage in Google Cloud Platform 333 15.8 Network in Google Cloud Platform 334 15.9 Data in Google Cloud Platform 334 15.10 Machine Learning in Google Cloud Platform 335 15.11 Conclusion 335 References 337 16 Case Study of Azure and Azure Security Practices 339 Navneet Bhardwaj, Abhik Banerjee and Agniswar Roy 16.1 Introduction 339 16.1.1 Azure Current Market Holding 340 16.1.2 The Forrester Wave 340 16.1.3 Gartner Magic Quadrant 340 16.2 Microsoft Azure—The Security Infrastructure 341 16.2.1 Azure Security Features and Tools 341 16.2.2 Network Security 342 16.3 Data Encryption 342 16.3.1 Data Encryption at Rest 342 16.3.2 Data Encryption at Transit 342 16.3.3 Asset and Inventory Management 343 16.3.4 Azure Marketplace 343 16.4 Azure Cloud Security Architecture 344 16.4.1 Working 344 16.4.2 Design Principles 344 16.4.2.1 Alignment of Security Policies 344 16.4.2.2 Building a Comprehensive Strategy 345 16.4.2.3 Simplicity Driven 345 16.4.2.4 Leveraging Native Controls 345 16.4.2.5 Identification-Based Authentication 345 16.4.2.6 Accountability 345 16.4.2.7 Embracing Automation 345 16.4.2.8 Stress on Information Protection 345 16.4.2.9 Continuous Evaluation 346 16.4.2.10 Skilled Workforce 346 16.5 Azure Architecture 346 16.5.1 Components 346 16.5.1.1 Azure Api Gateway 346 16.5.1.2 Azure Functions 346 16.5.2 Services 347 16.5.2.1 Azure Virtual Machine 347 16.5.2.2 Blob Storage 347 16.5.2.3 Azure Virtual Network 348 16.5.2.4 Content Delivery Network 348 16.5.2.5 Azure SQL Database 349 16.6 Features of Azure 350 16.6.1 Key Features 350 16.6.1.1 Data Resiliency 350 16.6.1.2 Data Security 350 16.6.1.3 BCDR Integration 350 16.6.1.4 Storage Management 351 16.6.1.5 Single Pane View 351 16.7 Common Azure Security Features 351 16.7.1 Security Center 351 16.7.2 Key Vault 351 16.7.3 Azure Active Directory 352 16.7.3.1 Application Management 352 16.7.3.2 Conditional Access 352 16.7.3.3 Device Identity Management 352 ​16.7.3. 4 Identity Protection 353 16.7.3.5 Azure Sentinel 353 16.7.3.6 Privileged Identity Management 354 16.7.3.7 Multifactor Authentication 354 16.7.3.8 Single Sign On 354 16.8 Conclusion 355 References 355 17 Nutanix Hybrid Cloud From Security Perspective 357 Abhik Banerjee, Agniswar Roy, Amar Kalvikatte and Navneet Bhardwaj 17.1 Introduction 357 17.2 Growth of Nutanix 358 17.2.1 Gartner Magic Quadrant 358 17.2.2 The Forrester Wave 358 17.2.3 Consumer Acquisition 359 17.2.4 Revenue 359 17.3 Introductory Concepts 361 17.3.1 Plane Concepts 361 17.3.1.1 Control Plane 361 17.3.1.2 Data Plane 361 17.3.2 Security Technical Implementation Guides 362 17.3.3 SaltStack and SCMA 362 17.4 Nutanix Hybrid Cloud 362 17.4.1 Prism 362 17.4.1.1 Prism Element 363 17.4.1.2 Prism Central 364 17.4.2 Acropolis 365 17.4.2.1 Distributed Storage Fabric 365 17.4.2.2 Ahv 367 17.5 Reinforcing AHV and Controller VM 367 17.6 Disaster Management and Recovery 368 17.6.1 Protection Domains and Consistent Groups 368 17.6.2 Nutanix DSF Replication of OpLog 369 17.6.3 DSF Snapshots and VmQueisced Snapshot Service 370 17.6.4 Nutanix Cerebro 370 17.7 Security and Policy Management on Nutanix Hybrid Cloud 371 17.7.1 Authentication on Nutanix 372 17.7.2 Nutanix Data Encryption 372 17.7.3 Security Policy Management 373 17.7.3.1 Enforcing a Policy 374 17.7.3.2 Priority of a Policy 374 17.7.3.3 Automated Enforcement 374 17.8 Network Security and Log Management 374 17.8.1 Segmented and Unsegmented Network 375 17.9 Conclusion 376 References 376 Part V: Policy Aspects 379 18 A Data Science Approach Based on User Interactions to Generate Access Control Policies for Large Collections of Documents 381 Jedidiah Yanez-Sierra, Arturo Diaz-Perez and Victor Sosa-Sosa 18.1 Introduction 381 18.2 Related Work 383 18.3 Network Science Theory 384 18.4 Approach to Spread Policies Using Networks Science 387 18.4.1 Finding the Most Relevant Spreaders 388 18.4.1.1 Weighting Users 389 18.4.1.2 Selecting the Top � Spreaders 390 18.4.2 Assign and Spread the Access Control Policies 390 18.4.2.1 Access Control Policies 391 18.4.2.2 Horizontal Spreading 391 18.4.2.3 Vertical Spreading (Bottom-Up) 392 18.4.2.4 Policies Refinement 395 18.4.3 Structural Complexity Analysis of CP-ABE Policies 395 18.4.3.1 Assessing the WSC for ABE Policies 396 18.4.3.2 Assessing the Policies Generated in the Spreading Process 397 18.4.4 Effectiveness Analysis 398 18.4.4.1 Evaluation Metrics 399 18.4.4.2 Adjusting the Interaction Graph to Assess Policy Effectiveness 400 18.4.4.3 Method to Complement the User Interactions (Synthetic Edges Generation) 400 18.4.5 Measuring Policy Effectiveness in the User Interaction Graph 403 18.4.5.1 Simple Node-Based Strategy 403 18.4.5.2 Weighted Node-Based Strategy 404 18.5 Evaluation 405 18.5.1 Dataset Description 405 18.5.2 Results of the Complexity Evaluation 406 18.5.3 Effectiveness Results From the Real Edges 407 18.5.4 Effectiveness Results Using Real and Synthetic Edges 408 18.5.4.1 Results of the Effectiveness Metrics for the Enhanced G + Graph 410 18.6 Conclusions 413 References 414 19 AI, ML, & Robotics in iSchools: An Academic Analysis for an Intelligent Societal Systems 417 P. K. Paul 19.1 Introduction 417 19.2 Objective 419 19.3 Methodology 420 19.3.1 iSchools, Technologies, and Artificial Intelligence, ML, and Robotics 420 19.4 Artificial Intelligence, ML, and Robotics: An Overview 427 19.5 Artificial Intelligence, ML, and Robotics as an Academic Program: A Case on iSchools—North American Region 428 19.6 Suggestions 431 19.7 Motivation and Future Works 435 19.8 Conclusion 435 References 436 Index 439

This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.