Machine learning techniques and analytics for cloud security:
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| Format: | Buch |
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| Sprache: | English |
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[S.l.]
John Wiley
2022
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| Schriftenreihe: | Advances in learning analytics for intelligent cloud-IOT systems
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxiii, 443 Seiten Diagramme |
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Datensatz im Suchindex
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| adam_text | Contents Preface Part I: Conceptual Aspects on Cloud and Applications of Machine Learning 1 Hybrid Cloud: A New Paradigm in Cloud Computing Moutnita Deb and Abantika Choudhury 1.1 Introduction 1.2 Hybrid Cloud 1.2.1 Architecture 1.2.2 Why Hybrid Cloud is Required? 1.2.3 Business and Hybrid Cloud 1.2.4 Things to Remember When Deploying Hybrid Cloud 1.3 Comparison Among Different Hybrid Cloud Providers 1.3.1 Cloud Storage and Backup Benefits 1.3.2 Pros and Cons of Different Service Providers 1.3.2.1 AWS Outpost 1.3.2.2 Microsoft Azure Stack 1.3.2.3 Google Cloud Anthos 1.3.3 Review on Storage of the Providers 1.3.3.1 AWS Outpost Storage 1.3.3.2 Google Cloud Anthos Storage 1.3.4 Pricing 1.4 Hybrid Cloud in Education 1.5 Significance of Hybrid Cloud Post-Pandemic 1.6 Security in Hybrid Cloud 1.6.1 Role of Human Error in Cloud Security 1.6.2 Handling Security Challenges 1.7 Use of AI in Hybrid Cloud 1.8 Future Research Direction 1.9 Conclusion References xix 1 3 3 5 6 6 7 8 9 11 11 12 12 12 13 13 13 15 15 15 16 18 18 19 21 22 22
vi 2 3 Contents Recognition of Differentially Expressed Glycan Structure of HlN1 Virus Using Unsupervised Learning Framework Shillpi Mishrra 2.1 Introduction 2.2 Proposed Methodology 2.3 Result 2.3.1 Description of Datasets 2.3.2 Analysis of Result 2.3.3 Validation of Results 2.3.3.1 T-Test (Statistical Validation) 2.3.3.2 Statistical Validation 2.3.4 Glycan Cloud 2.4 Conclusions and Future Work References Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) Subir Hazra, Alia Nikhat Khurshid and Akriti 3.1 Introduction 3.2 Related Methods 3.3 Methodology 3.3.1 Description 3.3.2 Flowchart 3.3.3 Algorithm 3.3.4 Interpretation of the Algorithm 3.3.5 Illustration 3.4 Result 3.4.1 Description of the Dataset 3.4.2 Result Analysis 3.4.3 Result Set Validation 3.5 Application inCloud Domain 3.6 Conclusion References Part II: Cloud Security Systems Using Machine Learning Techniques 4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology Soumen Santra, Partha Mukherjee and Arpan Deyasi 4.1 Introduction 4.2 Home AutomationSystem 4.2.1 Sensors 4.2.2 Protocols 4.2.3 Technologies 25 25 27 28 29 29 31 31 33 37 38 39 41 41 44 46 47 49 49 50 50 51 51 51 52 56 58 59 61 63 64 65 65 66 66
Contents 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 5 6 4.2.4 Advantages 4.2.5 Disadvantages Literature Review Role of Sensors and Microcontrollers in Smart Home Design Motivation of the Project Smart Informative and Command Accepting Interface Data Flow Diagram Components of Informative Interface Results 4.9.1 Circuit Design 4.9.2 LDR Data 4.9.3 API Data Conclusion Future Scope References 67 67 67 68 70 70 71 72 73 73 76 76 78 78 78 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security Anirban Bhowmik, Sunil Karforma and Joydeep Dey 5.1 Introduction 5.2 Literature Review 5.3 The Problem 5.4 Objectives and Contributions 5.5 Methodology 5.6 Results and Discussions 5.6.1 Statistical Analysis 5.6.2 Randomness Test of Key 5.6.3 Key Sensitivity Analysis 5.6.4 Security Analysis 5.6.5 Dataset Used on ANN 5.6.6 Comparisons 5.7 Conclusions References An Efficient Intrusion Detection System on Various Datasets Using Machine Learning Techniques Debraj Chatterjee 6.1 Introduction 6.2 Motivation and Justification of the Proposed Work 6.3 Terminology Related to IDS 6.3.1 Network 6.3.2 Network Traffic 6.3.3 Intrusion 6.3.4 IntrusionDetection System 6.3.4.1 Various Types of IDS 6.3.4.2 Working Methodology of IDS vii 81 81 85 86 86 87 91 93 94 95 96 96 98 99 99 103 103 104 105 105 105 106 106 108 108
viii Contents 6.3.4.3 Characteristics of IDS 6.3.4.4 Advantages of IDS 6.3.4.5 Disadvantages of IDS 6.3.5 Intrusion Prevention System (IPS) 6.3.5.1 Network-Based IntrusionPrevention System(NIPS) 6.3.5.2 Wireless Intrusion PreventionSystem (WIPS) 6.3.5.3 Network Behavior Analysis (NBA) 6.3.5.4 Host-Based Intrusion Prevention System (HIPS) 6.3.6 Comparison of IPS With IDS/Relation Between IDS and IPS 6.3.7 Different Methods of Evasion in Networks 6.4 Intrusion Attacks on Cloud Environment 6.5 Comparative Studies 6.6 Proposed Methodology 6.7 Result 6.8 Conclusion and Future Scope References 7 8 You Are Known by Your Mood: A Text-Based Sentiment Analysis for Cloud Security Abhijit Roy and Parthajit Roy 7.1 Introduction 7.2 Literature Review 7.3 Essential Prerequisites 7.3.1 Security Aspects 7.3.2 Machine Learning Tools 7.3.2.1 Naïve Bayes Classifier 7.3.2.2 Artificial Neural Network 7.4 Proposed Model 7.5 Experimental Setup 7.6 Results and Discussions 7.7 Application in Cloud Security 7.7.1 Ask an Intelligent Security Question 7.7.2 Homomorphic Data Storage 7.7.3 Information Diffusion 7.8 Conclusion and Future Scope References The State-of-the-Art in Zero-Knowledge Authentication Proof for Cloud Priyanka Ghosh 8.1 Introduction 8.2 Attacks and Countermeasures 8.2.1 Malware and Ransomware Breaches 8.2.2 Prevention of Distributing Denialof Service 8.2.3 Threat Detection 8.3 Zero-Knowledge Proof 109 110 111 111 111 112 112 112 112 113 114 116 121 122 125 126 129 129 131 133 133 135 135 136 136 138 139 142 142 142 144 144 145 149 149 153 154 154 154 154
Contents 8.4 8.5 8.6 9 Machine Learning for Cloud Computing 8.4.1 Types of Learning Algorithms 8.4.1.1 Supervised Learning 8.4.1.2 Supervised Learning Approach 8.4.1.3 Unsupervised Learning 8.4.2 Application on Machine Learning for Cloud Computing 8.4.2.1 Image Recognition 8.4.2.2 Speech Recognition 8.4.2.3 Medical Diagnosis 8.4.2.4 Learning Associations 8.4.2.5 Classification 8.4.2.6 Prediction 8.4.2.7 Extraction 8.4.2.8 Regression 8.4.2.9 Financial Services Zero-Knowledge Proof: Details 8.5.1 Comparative Study 8.5.1.1 Fiat-Shamir ZKP Protocol 8.5.2 Diffie-Hellman Key Exchange Algorithm 8.5.2.1 Discrete Logarithm Attack 8.5.2.2 Man-in-the-Middle Attack 8.5.3 ZKP Versioni 8.5.4 ZKP Version 2 8.5.5 Analysis 8.5.6 Cloud Security Architecture 8.5.7 Existing Cloud Computing Architectures 8.5.8 Issues With Current Clouds Conclusion References A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques Amartya Chakraborty, Suvendu Chattaraj, Sangita Karmakar and Shillpi Mishrra 9.1 Introduction 9.2 Literature Review 9.3 Motivation 9.4 System Overview 9.5 Data Description 9.6 Data Processing 9.7 Feature Extraction 9.8 Learning Techniques Used 9.8.1 Support Vector Machine 9.8.2 k-Nearest Neighbors 9.8.3 Decision Tree 9.8.4 Convolutional Neural Network ix 156 156 156 156 157 157 157 157 158 158 158 158 158 158 159 159 159 159 161 161 162 162 162 164 166 167 167 168 169 171 171 173 174 175 176 176 178 179 179 180 180 180
x Contents 9.9 Experimental Setup 9.10 Evaluation Metrics 9.11 Experimental Results 9.11.1 Observations in Comparison With State-of-the-Art 9.12 Application in Cloud Architecture 9.13 Conclusion References 10 An Intelligent System for Securing Network From Intrusion Detection and Prevention of Phishing Attack Using Machine Learning Approaches Sumit Bánik, Sagar Bánik and Anupam Mukherjee 10.1 Introduction 10.1.1 Types of Phishing 10.1.1.1 Spear Phishing 10.1.1.2 Whaling 10.1.1.3 Catphishing and Catfishing 10.1.1.4 Clone Phishing 10.1.1.5 Voice Phishing 10.1.2 Techniques of Phishing 10.1.2.1 Link Manipulation 10.1.2.2 Filter Evasion 10.1.2.3 Website Forgery 10.1.2.4 Covert Redirect 10.2 Literature Review 10.3 Materials and Methods 10.3.1 Dataset and Attributes 10.3.2 Proposed Methodology 10.3.2.1 Logistic Regression 10.3.2.2 Naive Bayes 10.3.2.3 Support Vector Machine 10.3.2.4 Voting Classification 10.4 Result Analysis 10.4.1 Analysis of Different Parameters for ML Models 10.4.2 Predictive Outcome Analysis in Phishing URLsDataset 10.4.3 Analysis of Performance Metrics 10.4.4 Statistical Analysis of Results 10.4.4.1 ANOVA: Two-Factor Without Replication 10.4.4.2 ANOVA: Single Factor 10.5 Conclusion References Part III: Cloud Security Analysis Using Machine Learning Techniques 11 Cloud Security Using Honeypot Network and Blockchain: A Review Smarta Sangui and Swarup Kr Ghosh 11.1 Introduction 182 183 185 187 188 189 190 193 193 195 195 195 195 196 196 196 196 196 196 197 197 199 199 199 202 202 203 203 204 204 205 206 210 210 210 210 211 213 215 215
Contents 11.2 11.3 11.4 11.6 11.7 Cloud Computing Overview 11.2.1 Types of Cloud Computing Services 11.2.1.1 Software as a Service 11.2.1.2 Infrastructure as a Service 11.2.1.3 Platform as a Service 11.2.2 Deployment Models of Cloud Computing 11.2.2.1 Public Cloud 11.2.2.2 Private Cloud 11.2.2.3 Community Cloud 11.2.2.4 Hybrid Cloud 11.2.3 Security Concerns in Cloud Computing 11.2.3.1 Data Breaches 11.2.3.2 Insufficient Change Control and Misconfiguration 11.2.3.3 Lack of Strategy and Security Architecture 11.2.3.4 Insufficient Identity, Credential, Access, and Key Management 11.2.3.5 Account Hijacking 11.2.3.6 Insider Threat 11.2.3.7 Insecure Interfaces and APIs 11.2.3.8 Weak Control Plane Honeypot System 11.3.1 VM (Virtual Machine) as Honeypot in the Cloud 11.3.2 Attack Sensing and Analyzing Framework 11.3.3 A Fuzzy Technique Against Fingerprinting Attacks 11.3.4 Detecting and Classifying Malicious Access 11.3.5 A Bayesian Defense Model for Deceptive Attack 11.3.6 Strategic Game Model for DDoS Attacks in Smart Grid Blockchain 11.4.1 Blockchain-Based Encrypted Cloud Storage 11.4.2 Cloud-Assisted EHR Sharing via Consortium Blockchain 11.4.3 Blockchain-Secured Cloud Storage 11.4.4 Blockchain and Edge Computing-Based Security Architecture 11.4.5 Data Provenance Architecture in Cloud Ecosystem Using Blockchain Comparative Analysis Conclusion References 12 Machine Learning-Based Security in Cloud Database—A Survey Utsav Vora, Jayleena Mahato, Hrishav Dasgupta, Anand Kumar and Swarup Kr Ghosh 12.1 Introduction 12.2 Security Threats and Attacks 12.3 Dataset Description 12.3.1
NSL-KDD Dataset 12.3.2 UNSW-NB15 Dataset xi 216 216 216 218 218 218 218 218 219 219 219 219 219 220 220 220 220 220 221 221 221 222 223 224 224 226 227 228 229 230 230 231 233 233 234 239 239 241 244 244 244
xii Contents 12.4 Machine Learning for Cloud Security 12.4.1 Supervised Learning Techniques 12.4.1.1 Support Vector Machine 12.4.1.2 Artificial Neural Network 12.4.1.3 Deep Learning 12.4.1.4 Random Forest 12.4.2 Unsupervised Learning Techniques 12.4.2.1 К-Means Clustering 12.4.2.2 Fuzzy С-Means Clustering 12.4.2.3 Expectation-Maximization Clustering 12.4.2.4 Cuckoo Search With Particle Swarm Optimization (PSO) 12.4.3 Hybrid Learning Techniques 12.4.3.1 HIDCC: Hybrid Intrusion Detection Approach in Cloud Computing 12.4.3.2 Clustering-Based Hybrid Model in Deep Learning Framework 12.4.3.3 К-Nearest Neighbor-Based Fuzzy C-Means Mechanism 12.4.3.4 K-Means Clustering Using Support Vector Machine 12.4.3.5 К-Nearest Neighbor-Based Artificial Neural Network Mechanism 12.4.3.6 Artificial Neural Network Fused With Support Vector Machine 12.4.3.7 Particle Swarm Optimization-Based Probabilistic Neural Network 12.5 Comparative Analysis 12.6 Conclusion References 13 Machine Learning Adversarial Attacks: A Survey Beyond Chandni Magoo and Puneet Garg 13.1 Introduction 13.2 Adversarial Learning 13.2.1 Concept 13.3 Taxonomy of Adversarial Attacks 13.3.1 Attacks Based on Knowledge 13.3.1.1 Black Box Attack (Transferable Attack) 13.3.1.2 White Box Attack 13.3.2 Attacks Based on Goals 13.3.2.1 Target Attacks 13.3.2.2 Non-Target Attacks 13.3.3 Attacks Based on Strategies 13.3.3.1 Poisoning Attacks 13.3.3.2 Evasion Attacks 245 245 245 247 249 250 251 252 253 253 254 256 256 257 258 260 260 261 261 262 264 267 271 271 272 272 273 273 273 274 275 275 275 275 275 276
Contents Textual-Based Attacks (NLP) 13.3.4.1 Character Level Attacks 13.3.4.2 Word-Level Attacks 13.3.4.3 Sentence-Level Attacks Review of Adversarial Attack Methods 13.4.1 L-BFGS 13.4.2 Feedforward Derivation Attack (Jacobian Attack) 13.4.3 Fast Gradient Sign Method 13.4.4 Methods of Different Text-Based Adversarial Attacks 13.4.5 Adversarial Attacks Methods Based on Language Models 13.4.6 Adversarial Attacks on Recommender Systems 13.4.6.1 Random Attack 13.4.6.2 Average Attack 13.4.6.3 Bandwagon Attack 13.4.6.4 Reverse Bandwagon Attack Adversarial Attacks on Cloud-Based Platforms Conclusion References 13.3.4 13.4 13.5 13.6 14 Protocols for Cloud Security Weijing You and Bo Chen 14.1 Introduction 14.2 System and Adversarial Model 14.2.1 System Model 14.2.2 Adversarial Model 14.3 Protocols for Data Protection in Secure Cloud Computing 14.3.1 Homomorphic Encryption 14.3.2 Searchable Encryption 14.3.3 Attribute-Based Encryption 14.3.4 Secure Multi-Party Computation 14.4 Protocols for Data Protection in Secure Cloud Storage 14.4.1 Proofs of Encryption 14.4.2 Secure Message-Locked Encryption 14.4.3 Proofs of Storage 14.4.4 Proofs of Ownership 14.4.5 Proofs of Reliability 14.5 Protocols for Secure Cloud Systems 14.6 Protocols for Cloud Security in the Future 14.7 Conclusion References xiii 276 276 276 276 276 277 277 278 278 284 284 284 286 286 286 287 288 288 293 293 295 295 295 296 297 298 299 300 301 301 303 303 305 306 309 309 310 311
xiv Contents Part IV: Case Studies Focused on Cloud Security 15 A Study on Google Cloud Platform (GCP) and Its Security Agniswar Roy, Abhik Banerjee and Navneet Bhardwaj 15.1 Introduction 15.1.1 Google Cloud Platform CurrentMarket Holding 15.1.1.1 The Forrester Wave 15.1.1.2 Gartner Magic Quadrant 15.1.2 Google Cloud Platform Work Distribution 15.1.2.1 SaaS 15.1.2.2 PaaS 15.1.2.3 laaS 15.1.2.4 On-Premise 15.2 Google Cloud Platforms Security Features Basic Overview 15.2.1 Physical Premises Security 15.2.2 Hardware Security 15.2.3 Inter-ServiceSecurity 15.2.4 Data Security 15.2.5 Internet Security 15.2.6 In-Software Security 15.2.7 End User Access Security 15.3 Google Cloud Platforms Architecture 15.3.1 Geographic Zone 15.3.2 Resource Management 15.3.2.1 IAM 15.3.2.2 Roles 15.3.2.3 Billing 15.4 Key Security Features 15.4.1 IAP 15.4.2 Compliance 15.4.3 Policy Analyzer 15.4.4 Security Command Center 15.4.4.1 Standard Tier 15.4.4.2 Premium Tier 15.4.5 Data Loss Protection 15.4.6 Key Management 15.4.7 Secret Manager 15.4.8 Monitoring 15.5 Key Application Features 15.5.1 Stackdriver (Currently Operations) 15.5.1.1 Profiler 15.5.1.2 Cloud Debugger 15.5.1.3 Trace 15.5.2 Network 15.5.3 Virtual Machine Specifications 313 315 315 316 317 317 317 318 318 318 318 318 319 319 319 320 320 320 321 321 321 322 322 323 323 324 324 325 326 326 326 326 329 329 330 330 330 330 330 330 331 331 332
Contents xv 15.5.4 Preemptible VMs Computation in Google Cloud Platform 15.6.1 Compute Engine 15.6.2 App Engine 15.6.3 Container Engine 15.6.4 Cloud Functions 15.7 Storage in Google Cloud Platform 15.8 Network in Google Cloud Platform 15.9 Data in Google Cloud Platform 15.10 Machine Learning in Google Cloud Platform 15.11 Conclusion References 332 332 332 333 333 333 333 334 334 335 335 337 Case Study of Azure and Azure Security Practices Navneet Bhardwaj, Abhik Banerjee and Agniswar Roy 16.1 Introduction 16.1.1 Azure Current Market Holding 16.1.2 The Forrester Wave 16.1.3 Gartner Magic Quadrant 16.2 Microsoft Azure—The Security Infrastructure 16.2.1 Azure Security Features and Tools 16.2.2 Network Security 16.3 Data Encryption 16.3.1 Data Encryption at Rest 16.3.2 Data Encryption at Transit 16.3.3 Asset and Inventory Management 16.3.4 Azure Marketplace 16.4 Azure Cloud Security Architecture 16.4.1 Working 16.4.2 Design Principles 16.4.2.1 Alignment of Security Policies 16.4.2.2 Building a Comprehensive Strategy 16.4.2.3 Simplicity Driven 16.4.2.4 Leveraging Native Controls 16.4.2.5 Identification-Based Authentication 16.4.2.6 Accountability 16.4.2.7 Embracing Automation 16.4.2.8 Stress on Information Protection 16.4.2.9 Continuous Evaluation 16.4.2.10 Skilled Workforce 16.5 Azure Architecture 16.5.1 Components 16.5.1.1 Azure Api Gateway 16.5.1.2 Azure Functions 16.5.2 Services 16.5.2.1 Azure Virtual Machine 339 15.6 339 340 340 340 341 341 342 342 342 342 343 343 344 344 344 344 345 345 345 345 345 345 345 346 346 346 346 346 346 347 347
xvi Contents 16.5.2.2 Blob Storage 16.5.2.3 Azure Virtual Network 16.5.2.4 Content Delivery Network 16.5.2.5 Azure SQL Database 16.6 Features of Azure 16.6.1 Key Features 16.6.1.1 Data Resiliency 16.6.1.2 Data Security 16.6.1.3 BCDR Integration 16.6.1.4 Storage Management 16.6.1.5 Single Pane View 16.7 Common Azure Security Features 16.7.1 Security Center 16.7.2 Key Vault 16.7.3 Azure Active Directory 16.7.3.1 Application Management 16.7.3.2 Conditional Access 16.7.3.3 Device Identity Management 16.7.3.4 Identity Protection 16.7.3.5 Azure Sentinel 16.7.3.6 Privileged Identity Management 16.7.3.7 Multifactor Authentication 16.7.3.8 Single Sign On 16.8 Conclusion References 17 Nutanix Hybrid Cloud From Security Perspective Abhik Banerjee, Agniswar Roy, Amar Kalvikatte and Navneet Bhardwaj 17.1 Introduction 17.2 Growth of Nutanix 17.2.1 Gartner Magic Quadrant 17.2.2 The Forrester Wave 17.2.3 Consumer Acquisition 17.2.4 Revenue 17.3 Introductory Concepts 17.3.1 Plane Concepts 17.3.1.1 Control Plane 17.3.1.2 Data Plane 17.3.2 Security Technical Implementation Guides 17.3.3 SaltStack and SCMA 17.4 Nutanix Hybrid Cloud 17.4.1 Prism 17.4.1.1 Prism Element 17.4.1.2 Prism Central 17.4.2 Acropolis 17.4.2.1 Distributed Storage Fabric 347 348 348 349 350 350 350 350 350 351 351 351 351 351 352 352 352 352 353 353 354 354 354 355 355 357 357 358 358 358 359 359 361 361 361 361 362 362 362 362 363 364 365 365
Contents 17.5 17.6 17.7 17.8 17.9 17.4.2.2 AHV Reinforcing AHV and Controller VM Disaster Management and Recovery 17.6.1 Protection Domains and Consistent Groups 17.6.2 Nutanix DSF Replication of OpLog 17.6.3 DSF Snapshots and VmQueisced Snapshot Service 17.6.4 Nutanix Cerebro Security and Policy Management on Nutanix Hybrid Cloud 17.7.1 Authentication on Nutanix 17.7.2 Nutanix Data Encryption 17.7.3 Security Policy Management 17.7.3.1 Enforcing a Policy 17.7.3.2 Priority of a Policy 17.7.3.3 Automated Enforcement Network Security and Log Management 17.8.1 Segmented and Unsegmented Network Conclusion References Part V: Policy Aspects 18 A Data Science Approach Based on User Interactions to Generate Access Control Policies for Large Collections of Documents Jedidiah Yanez-Sierra, Arturo Diaz-Perez and Victor Sosa-Sosa 18.1 Introduction 18.2 Related Work 18.3 Network Science Theory 18.4 Approach to Spread Policies Using Networks Science 18.4.1 Finding the Most Relevant Spreaders 18.4.1.1 Weighting Users 18.4.1.2 Selecting the Top P Spreaders 18.4.2 Assign and Spread the Access Control Policies 18.4.2.1 Access Control Policies 18.4.2.2 Horizontal Spreading 18.4.2.3 Vertical Spreading (Bottom-Up) 18.4.2.4 Policies Refinement 18.4.3 Structural Complexity Analysis of CP-ABE Policies 18.4.3.1 Assessing the WSC for ABE Policies 18.4.3.2 Assessing the Policies Generated in the Spreading Process 18.4.4 Effectiveness Analysis 18.4.4.1 Evaluation Metrics 18.4.4.2 Adjusting the Interaction Graph to Assess Policy Effectiveness 18.4.4.3 Method to Complement the User Interactions
(Synthetic Edges Generation) xvii 367 367 368 368 369 370 370 371 372 372 373 374 374 374 374 375 376 376 379 381 381 383 384 387 388 389 390 390 391 391 392 395 395 396 397 398 399 400 400
xviii Contents Measuring Policy Effectiveness in the User Interaction Graph 18.4.5.1 Simple Node-Based Strategy 18.4.5.2 Weighted Node-Based Strategy Evaluation 18.5.1 Dataset Description 18.5.2 Results of the Complexity Evaluation 18.5.3 Effectiveness Results From the Real Edges 18.5.4 Effectiveness Results Using Real and Synthetic Edges 18.5.4.1 Results of the Effectiveness Metrics for the Enhanced G+ Graph Conclusions References 18.4.5 18.5 18.6 19 AI, ML, Robotics in iSchools: An Academic Analysis for an Intelligent Societal Systems P. K. Paul 19.1 Introduction 19.2 Objective 19.3 Methodology 19.3.1 iSchools, Technologies, and Artificial Intelligence, ML, and Robotics 19.4 Artificial Intelligence, ML, and Robotics:An Overview 19.5 Artificial Intelligence, ML, and Roboticsas an Academic Program: A Case on iSchools—North American Region 19.6 Suggestions 19.7 Motivation and Future Works 19.8 Conclusion References Index 403 403 404 405 405 406 407 408 410 413 414 417 417 419 420 420 427 428 431 435 435 436 439
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Contents Preface Part I: Conceptual Aspects on Cloud and Applications of Machine Learning 1 Hybrid Cloud: A New Paradigm in Cloud Computing Moutnita Deb and Abantika Choudhury 1.1 Introduction 1.2 Hybrid Cloud 1.2.1 Architecture 1.2.2 Why Hybrid Cloud is Required? 1.2.3 Business and Hybrid Cloud 1.2.4 Things to Remember When Deploying Hybrid Cloud 1.3 Comparison Among Different Hybrid Cloud Providers 1.3.1 Cloud Storage and Backup Benefits 1.3.2 Pros and Cons of Different Service Providers 1.3.2.1 AWS Outpost 1.3.2.2 Microsoft Azure Stack 1.3.2.3 Google Cloud Anthos 1.3.3 Review on Storage of the Providers 1.3.3.1 AWS Outpost Storage 1.3.3.2 Google Cloud Anthos Storage 1.3.4 Pricing 1.4 Hybrid Cloud in Education 1.5 Significance of Hybrid Cloud Post-Pandemic 1.6 Security in Hybrid Cloud 1.6.1 Role of Human Error in Cloud Security 1.6.2 Handling Security Challenges 1.7 Use of AI in Hybrid Cloud 1.8 Future Research Direction 1.9 Conclusion References xix 1 3 3 5 6 6 7 8 9 11 11 12 12 12 13 13 13 15 15 15 16 18 18 19 21 22 22
vi 2 3 Contents Recognition of Differentially Expressed Glycan Structure of HlN1 Virus Using Unsupervised Learning Framework Shillpi Mishrra 2.1 Introduction 2.2 Proposed Methodology 2.3 Result 2.3.1 Description of Datasets 2.3.2 Analysis of Result 2.3.3 Validation of Results 2.3.3.1 T-Test (Statistical Validation) 2.3.3.2 Statistical Validation 2.3.4 Glycan Cloud 2.4 Conclusions and Future Work References Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) Subir Hazra, Alia Nikhat Khurshid and Akriti 3.1 Introduction 3.2 Related Methods 3.3 Methodology 3.3.1 Description 3.3.2 Flowchart 3.3.3 Algorithm 3.3.4 Interpretation of the Algorithm 3.3.5 Illustration 3.4 Result 3.4.1 Description of the Dataset 3.4.2 Result Analysis 3.4.3 Result Set Validation 3.5 Application inCloud Domain 3.6 Conclusion References Part II: Cloud Security Systems Using Machine Learning Techniques 4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology Soumen Santra, Partha Mukherjee and Arpan Deyasi 4.1 Introduction 4.2 Home AutomationSystem 4.2.1 Sensors 4.2.2 Protocols 4.2.3 Technologies 25 25 27 28 29 29 31 31 33 37 38 39 41 41 44 46 47 49 49 50 50 51 51 51 52 56 58 59 61 63 64 65 65 66 66
Contents 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 5 6 4.2.4 Advantages 4.2.5 Disadvantages Literature Review Role of Sensors and Microcontrollers in Smart Home Design Motivation of the Project Smart Informative and Command Accepting Interface Data Flow Diagram Components of Informative Interface Results 4.9.1 Circuit Design 4.9.2 LDR Data 4.9.3 API Data Conclusion Future Scope References 67 67 67 68 70 70 71 72 73 73 76 76 78 78 78 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security Anirban Bhowmik, Sunil Karforma and Joydeep Dey 5.1 Introduction 5.2 Literature Review 5.3 The Problem 5.4 Objectives and Contributions 5.5 Methodology 5.6 Results and Discussions 5.6.1 Statistical Analysis 5.6.2 Randomness Test of Key 5.6.3 Key Sensitivity Analysis 5.6.4 Security Analysis 5.6.5 Dataset Used on ANN 5.6.6 Comparisons 5.7 Conclusions References An Efficient Intrusion Detection System on Various Datasets Using Machine Learning Techniques Debraj Chatterjee 6.1 Introduction 6.2 Motivation and Justification of the Proposed Work 6.3 Terminology Related to IDS 6.3.1 Network 6.3.2 Network Traffic 6.3.3 Intrusion 6.3.4 IntrusionDetection System 6.3.4.1 Various Types of IDS 6.3.4.2 Working Methodology of IDS vii 81 81 85 86 86 87 91 93 94 95 96 96 98 99 99 103 103 104 105 105 105 106 106 108 108
viii Contents 6.3.4.3 Characteristics of IDS 6.3.4.4 Advantages of IDS 6.3.4.5 Disadvantages of IDS 6.3.5 Intrusion Prevention System (IPS) 6.3.5.1 Network-Based IntrusionPrevention System(NIPS) 6.3.5.2 Wireless Intrusion PreventionSystem (WIPS) 6.3.5.3 Network Behavior Analysis (NBA) 6.3.5.4 Host-Based Intrusion Prevention System (HIPS) 6.3.6 Comparison of IPS With IDS/Relation Between IDS and IPS 6.3.7 Different Methods of Evasion in Networks 6.4 Intrusion Attacks on Cloud Environment 6.5 Comparative Studies 6.6 Proposed Methodology 6.7 Result 6.8 Conclusion and Future Scope References 7 8 You Are Known by Your Mood: A Text-Based Sentiment Analysis for Cloud Security Abhijit Roy and Parthajit Roy 7.1 Introduction 7.2 Literature Review 7.3 Essential Prerequisites 7.3.1 Security Aspects 7.3.2 Machine Learning Tools 7.3.2.1 Naïve Bayes Classifier 7.3.2.2 Artificial Neural Network 7.4 Proposed Model 7.5 Experimental Setup 7.6 Results and Discussions 7.7 Application in Cloud Security 7.7.1 Ask an Intelligent Security Question 7.7.2 Homomorphic Data Storage 7.7.3 Information Diffusion 7.8 Conclusion and Future Scope References The State-of-the-Art in Zero-Knowledge Authentication Proof for Cloud Priyanka Ghosh 8.1 Introduction 8.2 Attacks and Countermeasures 8.2.1 Malware and Ransomware Breaches 8.2.2 Prevention of Distributing Denialof Service 8.2.3 Threat Detection 8.3 Zero-Knowledge Proof 109 110 111 111 111 112 112 112 112 113 114 116 121 122 125 126 129 129 131 133 133 135 135 136 136 138 139 142 142 142 144 144 145 149 149 153 154 154 154 154
Contents 8.4 8.5 8.6 9 Machine Learning for Cloud Computing 8.4.1 Types of Learning Algorithms 8.4.1.1 Supervised Learning 8.4.1.2 Supervised Learning Approach 8.4.1.3 Unsupervised Learning 8.4.2 Application on Machine Learning for Cloud Computing 8.4.2.1 Image Recognition 8.4.2.2 Speech Recognition 8.4.2.3 Medical Diagnosis 8.4.2.4 Learning Associations 8.4.2.5 Classification 8.4.2.6 Prediction 8.4.2.7 Extraction 8.4.2.8 Regression 8.4.2.9 Financial Services Zero-Knowledge Proof: Details 8.5.1 Comparative Study 8.5.1.1 Fiat-Shamir ZKP Protocol 8.5.2 Diffie-Hellman Key Exchange Algorithm 8.5.2.1 Discrete Logarithm Attack 8.5.2.2 Man-in-the-Middle Attack 8.5.3 ZKP Versioni 8.5.4 ZKP Version 2 8.5.5 Analysis 8.5.6 Cloud Security Architecture 8.5.7 Existing Cloud Computing Architectures 8.5.8 Issues With Current Clouds Conclusion References A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques Amartya Chakraborty, Suvendu Chattaraj, Sangita Karmakar and Shillpi Mishrra 9.1 Introduction 9.2 Literature Review 9.3 Motivation 9.4 System Overview 9.5 Data Description 9.6 Data Processing 9.7 Feature Extraction 9.8 Learning Techniques Used 9.8.1 Support Vector Machine 9.8.2 k-Nearest Neighbors 9.8.3 Decision Tree 9.8.4 Convolutional Neural Network ix 156 156 156 156 157 157 157 157 158 158 158 158 158 158 159 159 159 159 161 161 162 162 162 164 166 167 167 168 169 171 171 173 174 175 176 176 178 179 179 180 180 180
x Contents 9.9 Experimental Setup 9.10 Evaluation Metrics 9.11 Experimental Results 9.11.1 Observations in Comparison With State-of-the-Art 9.12 Application in Cloud Architecture 9.13 Conclusion References 10 An Intelligent System for Securing Network From Intrusion Detection and Prevention of Phishing Attack Using Machine Learning Approaches Sumit Bánik, Sagar Bánik and Anupam Mukherjee 10.1 Introduction 10.1.1 Types of Phishing 10.1.1.1 Spear Phishing 10.1.1.2 Whaling 10.1.1.3 Catphishing and Catfishing 10.1.1.4 Clone Phishing 10.1.1.5 Voice Phishing 10.1.2 Techniques of Phishing 10.1.2.1 Link Manipulation 10.1.2.2 Filter Evasion 10.1.2.3 Website Forgery 10.1.2.4 Covert Redirect 10.2 Literature Review 10.3 Materials and Methods 10.3.1 Dataset and Attributes 10.3.2 Proposed Methodology 10.3.2.1 Logistic Regression 10.3.2.2 Naive Bayes 10.3.2.3 Support Vector Machine 10.3.2.4 Voting Classification 10.4 Result Analysis 10.4.1 Analysis of Different Parameters for ML Models 10.4.2 Predictive Outcome Analysis in Phishing URLsDataset 10.4.3 Analysis of Performance Metrics 10.4.4 Statistical Analysis of Results 10.4.4.1 ANOVA: Two-Factor Without Replication 10.4.4.2 ANOVA: Single Factor 10.5 Conclusion References Part III: Cloud Security Analysis Using Machine Learning Techniques 11 Cloud Security Using Honeypot Network and Blockchain: A Review Smarta Sangui and Swarup Kr Ghosh 11.1 Introduction 182 183 185 187 188 189 190 193 193 195 195 195 195 196 196 196 196 196 196 197 197 199 199 199 202 202 203 203 204 204 205 206 210 210 210 210 211 213 215 215
Contents 11.2 11.3 11.4 11.6 11.7 Cloud Computing Overview 11.2.1 Types of Cloud Computing Services 11.2.1.1 Software as a Service 11.2.1.2 Infrastructure as a Service 11.2.1.3 Platform as a Service 11.2.2 Deployment Models of Cloud Computing 11.2.2.1 Public Cloud 11.2.2.2 Private Cloud 11.2.2.3 Community Cloud 11.2.2.4 Hybrid Cloud 11.2.3 Security Concerns in Cloud Computing 11.2.3.1 Data Breaches 11.2.3.2 Insufficient Change Control and Misconfiguration 11.2.3.3 Lack of Strategy and Security Architecture 11.2.3.4 Insufficient Identity, Credential, Access, and Key Management 11.2.3.5 Account Hijacking 11.2.3.6 Insider Threat 11.2.3.7 Insecure Interfaces and APIs 11.2.3.8 Weak Control Plane Honeypot System 11.3.1 VM (Virtual Machine) as Honeypot in the Cloud 11.3.2 Attack Sensing and Analyzing Framework 11.3.3 A Fuzzy Technique Against Fingerprinting Attacks 11.3.4 Detecting and Classifying Malicious Access 11.3.5 A Bayesian Defense Model for Deceptive Attack 11.3.6 Strategic Game Model for DDoS Attacks in Smart Grid Blockchain 11.4.1 Blockchain-Based Encrypted Cloud Storage 11.4.2 Cloud-Assisted EHR Sharing via Consortium Blockchain 11.4.3 Blockchain-Secured Cloud Storage 11.4.4 Blockchain and Edge Computing-Based Security Architecture 11.4.5 Data Provenance Architecture in Cloud Ecosystem Using Blockchain Comparative Analysis Conclusion References 12 Machine Learning-Based Security in Cloud Database—A Survey Utsav Vora, Jayleena Mahato, Hrishav Dasgupta, Anand Kumar and Swarup Kr Ghosh 12.1 Introduction 12.2 Security Threats and Attacks 12.3 Dataset Description 12.3.1
NSL-KDD Dataset 12.3.2 UNSW-NB15 Dataset xi 216 216 216 218 218 218 218 218 219 219 219 219 219 220 220 220 220 220 221 221 221 222 223 224 224 226 227 228 229 230 230 231 233 233 234 239 239 241 244 244 244
xii Contents 12.4 Machine Learning for Cloud Security 12.4.1 Supervised Learning Techniques 12.4.1.1 Support Vector Machine 12.4.1.2 Artificial Neural Network 12.4.1.3 Deep Learning 12.4.1.4 Random Forest 12.4.2 Unsupervised Learning Techniques 12.4.2.1 К-Means Clustering 12.4.2.2 Fuzzy С-Means Clustering 12.4.2.3 Expectation-Maximization Clustering 12.4.2.4 Cuckoo Search With Particle Swarm Optimization (PSO) 12.4.3 Hybrid Learning Techniques 12.4.3.1 HIDCC: Hybrid Intrusion Detection Approach in Cloud Computing 12.4.3.2 Clustering-Based Hybrid Model in Deep Learning Framework 12.4.3.3 К-Nearest Neighbor-Based Fuzzy C-Means Mechanism 12.4.3.4 K-Means Clustering Using Support Vector Machine 12.4.3.5 К-Nearest Neighbor-Based Artificial Neural Network Mechanism 12.4.3.6 Artificial Neural Network Fused With Support Vector Machine 12.4.3.7 Particle Swarm Optimization-Based Probabilistic Neural Network 12.5 Comparative Analysis 12.6 Conclusion References 13 Machine Learning Adversarial Attacks: A Survey Beyond Chandni Magoo and Puneet Garg 13.1 Introduction 13.2 Adversarial Learning 13.2.1 Concept 13.3 Taxonomy of Adversarial Attacks 13.3.1 Attacks Based on Knowledge 13.3.1.1 Black Box Attack (Transferable Attack) 13.3.1.2 White Box Attack 13.3.2 Attacks Based on Goals 13.3.2.1 Target Attacks 13.3.2.2 Non-Target Attacks 13.3.3 Attacks Based on Strategies 13.3.3.1 Poisoning Attacks 13.3.3.2 Evasion Attacks 245 245 245 247 249 250 251 252 253 253 254 256 256 257 258 260 260 261 261 262 264 267 271 271 272 272 273 273 273 274 275 275 275 275 275 276
Contents Textual-Based Attacks (NLP) 13.3.4.1 Character Level Attacks 13.3.4.2 Word-Level Attacks 13.3.4.3 Sentence-Level Attacks Review of Adversarial Attack Methods 13.4.1 L-BFGS 13.4.2 Feedforward Derivation Attack (Jacobian Attack) 13.4.3 Fast Gradient Sign Method 13.4.4 Methods of Different Text-Based Adversarial Attacks 13.4.5 Adversarial Attacks Methods Based on Language Models 13.4.6 Adversarial Attacks on Recommender Systems 13.4.6.1 Random Attack 13.4.6.2 Average Attack 13.4.6.3 Bandwagon Attack 13.4.6.4 Reverse Bandwagon Attack Adversarial Attacks on Cloud-Based Platforms Conclusion References 13.3.4 13.4 13.5 13.6 14 Protocols for Cloud Security Weijing You and Bo Chen 14.1 Introduction 14.2 System and Adversarial Model 14.2.1 System Model 14.2.2 Adversarial Model 14.3 Protocols for Data Protection in Secure Cloud Computing 14.3.1 Homomorphic Encryption 14.3.2 Searchable Encryption 14.3.3 Attribute-Based Encryption 14.3.4 Secure Multi-Party Computation 14.4 Protocols for Data Protection in Secure Cloud Storage 14.4.1 Proofs of Encryption 14.4.2 Secure Message-Locked Encryption 14.4.3 Proofs of Storage 14.4.4 Proofs of Ownership 14.4.5 Proofs of Reliability 14.5 Protocols for Secure Cloud Systems 14.6 Protocols for Cloud Security in the Future 14.7 Conclusion References xiii 276 276 276 276 276 277 277 278 278 284 284 284 286 286 286 287 288 288 293 293 295 295 295 296 297 298 299 300 301 301 303 303 305 306 309 309 310 311
xiv Contents Part IV: Case Studies Focused on Cloud Security 15 A Study on Google Cloud Platform (GCP) and Its Security Agniswar Roy, Abhik Banerjee and Navneet Bhardwaj 15.1 Introduction 15.1.1 Google Cloud Platform CurrentMarket Holding 15.1.1.1 The Forrester Wave 15.1.1.2 Gartner Magic Quadrant 15.1.2 Google Cloud Platform Work Distribution 15.1.2.1 SaaS 15.1.2.2 PaaS 15.1.2.3 laaS 15.1.2.4 On-Premise 15.2 Google Cloud Platforms Security Features Basic Overview 15.2.1 Physical Premises Security 15.2.2 Hardware Security 15.2.3 Inter-ServiceSecurity 15.2.4 Data Security 15.2.5 Internet Security 15.2.6 In-Software Security 15.2.7 End User Access Security 15.3 Google Cloud Platforms Architecture 15.3.1 Geographic Zone 15.3.2 Resource Management 15.3.2.1 IAM 15.3.2.2 Roles 15.3.2.3 Billing 15.4 Key Security Features 15.4.1 IAP 15.4.2 Compliance 15.4.3 Policy Analyzer 15.4.4 Security Command Center 15.4.4.1 Standard Tier 15.4.4.2 Premium Tier 15.4.5 Data Loss Protection 15.4.6 Key Management 15.4.7 Secret Manager 15.4.8 Monitoring 15.5 Key Application Features 15.5.1 Stackdriver (Currently Operations) 15.5.1.1 Profiler 15.5.1.2 Cloud Debugger 15.5.1.3 Trace 15.5.2 Network 15.5.3 Virtual Machine Specifications 313 315 315 316 317 317 317 318 318 318 318 318 319 319 319 320 320 320 321 321 321 322 322 323 323 324 324 325 326 326 326 326 329 329 330 330 330 330 330 330 331 331 332
Contents xv 15.5.4 Preemptible VMs Computation in Google Cloud Platform 15.6.1 Compute Engine 15.6.2 App Engine 15.6.3 Container Engine 15.6.4 Cloud Functions 15.7 Storage in Google Cloud Platform 15.8 Network in Google Cloud Platform 15.9 Data in Google Cloud Platform 15.10 Machine Learning in Google Cloud Platform 15.11 Conclusion References 332 332 332 333 333 333 333 334 334 335 335 337 Case Study of Azure and Azure Security Practices Navneet Bhardwaj, Abhik Banerjee and Agniswar Roy 16.1 Introduction 16.1.1 Azure Current Market Holding 16.1.2 The Forrester Wave 16.1.3 Gartner Magic Quadrant 16.2 Microsoft Azure—The Security Infrastructure 16.2.1 Azure Security Features and Tools 16.2.2 Network Security 16.3 Data Encryption 16.3.1 Data Encryption at Rest 16.3.2 Data Encryption at Transit 16.3.3 Asset and Inventory Management 16.3.4 Azure Marketplace 16.4 Azure Cloud Security Architecture 16.4.1 Working 16.4.2 Design Principles 16.4.2.1 Alignment of Security Policies 16.4.2.2 Building a Comprehensive Strategy 16.4.2.3 Simplicity Driven 16.4.2.4 Leveraging Native Controls 16.4.2.5 Identification-Based Authentication 16.4.2.6 Accountability 16.4.2.7 Embracing Automation 16.4.2.8 Stress on Information Protection 16.4.2.9 Continuous Evaluation 16.4.2.10 Skilled Workforce 16.5 Azure Architecture 16.5.1 Components 16.5.1.1 Azure Api Gateway 16.5.1.2 Azure Functions 16.5.2 Services 16.5.2.1 Azure Virtual Machine 339 15.6 339 340 340 340 341 341 342 342 342 342 343 343 344 344 344 344 345 345 345 345 345 345 345 346 346 346 346 346 346 347 347
xvi Contents 16.5.2.2 Blob Storage 16.5.2.3 Azure Virtual Network 16.5.2.4 Content Delivery Network 16.5.2.5 Azure SQL Database 16.6 Features of Azure 16.6.1 Key Features 16.6.1.1 Data Resiliency 16.6.1.2 Data Security 16.6.1.3 BCDR Integration 16.6.1.4 Storage Management 16.6.1.5 Single Pane View 16.7 Common Azure Security Features 16.7.1 Security Center 16.7.2 Key Vault 16.7.3 Azure Active Directory 16.7.3.1 Application Management 16.7.3.2 Conditional Access 16.7.3.3 Device Identity Management 16.7.3.4 Identity Protection 16.7.3.5 Azure Sentinel 16.7.3.6 Privileged Identity Management 16.7.3.7 Multifactor Authentication 16.7.3.8 Single Sign On 16.8 Conclusion References 17 Nutanix Hybrid Cloud From Security Perspective Abhik Banerjee, Agniswar Roy, Amar Kalvikatte and Navneet Bhardwaj 17.1 Introduction 17.2 Growth of Nutanix 17.2.1 Gartner Magic Quadrant 17.2.2 The Forrester Wave 17.2.3 Consumer Acquisition 17.2.4 Revenue 17.3 Introductory Concepts 17.3.1 Plane Concepts 17.3.1.1 Control Plane 17.3.1.2 Data Plane 17.3.2 Security Technical Implementation Guides 17.3.3 SaltStack and SCMA 17.4 Nutanix Hybrid Cloud 17.4.1 Prism 17.4.1.1 Prism Element 17.4.1.2 Prism Central 17.4.2 Acropolis 17.4.2.1 Distributed Storage Fabric 347 348 348 349 350 350 350 350 350 351 351 351 351 351 352 352 352 352 353 353 354 354 354 355 355 357 357 358 358 358 359 359 361 361 361 361 362 362 362 362 363 364 365 365
Contents 17.5 17.6 17.7 17.8 17.9 17.4.2.2 AHV Reinforcing AHV and Controller VM Disaster Management and Recovery 17.6.1 Protection Domains and Consistent Groups 17.6.2 Nutanix DSF Replication of OpLog 17.6.3 DSF Snapshots and VmQueisced Snapshot Service 17.6.4 Nutanix Cerebro Security and Policy Management on Nutanix Hybrid Cloud 17.7.1 Authentication on Nutanix 17.7.2 Nutanix Data Encryption 17.7.3 Security Policy Management 17.7.3.1 Enforcing a Policy 17.7.3.2 Priority of a Policy 17.7.3.3 Automated Enforcement Network Security and Log Management 17.8.1 Segmented and Unsegmented Network Conclusion References Part V: Policy Aspects 18 A Data Science Approach Based on User Interactions to Generate Access Control Policies for Large Collections of Documents Jedidiah Yanez-Sierra, Arturo Diaz-Perez and Victor Sosa-Sosa 18.1 Introduction 18.2 Related Work 18.3 Network Science Theory 18.4 Approach to Spread Policies Using Networks Science 18.4.1 Finding the Most Relevant Spreaders 18.4.1.1 Weighting Users 18.4.1.2 Selecting the Top P Spreaders 18.4.2 Assign and Spread the Access Control Policies 18.4.2.1 Access Control Policies 18.4.2.2 Horizontal Spreading 18.4.2.3 Vertical Spreading (Bottom-Up) 18.4.2.4 Policies Refinement 18.4.3 Structural Complexity Analysis of CP-ABE Policies 18.4.3.1 Assessing the WSC for ABE Policies 18.4.3.2 Assessing the Policies Generated in the Spreading Process 18.4.4 Effectiveness Analysis 18.4.4.1 Evaluation Metrics 18.4.4.2 Adjusting the Interaction Graph to Assess Policy Effectiveness 18.4.4.3 Method to Complement the User Interactions
(Synthetic Edges Generation) xvii 367 367 368 368 369 370 370 371 372 372 373 374 374 374 374 375 376 376 379 381 381 383 384 387 388 389 390 390 391 391 392 395 395 396 397 398 399 400 400
xviii Contents Measuring Policy Effectiveness in the User Interaction Graph 18.4.5.1 Simple Node-Based Strategy 18.4.5.2 Weighted Node-Based Strategy Evaluation 18.5.1 Dataset Description 18.5.2 Results of the Complexity Evaluation 18.5.3 Effectiveness Results From the Real Edges 18.5.4 Effectiveness Results Using Real and Synthetic Edges 18.5.4.1 Results of the Effectiveness Metrics for the Enhanced G+ Graph Conclusions References 18.4.5 18.5 18.6 19 AI, ML, Robotics in iSchools: An Academic Analysis for an Intelligent Societal Systems P. K. Paul 19.1 Introduction 19.2 Objective 19.3 Methodology 19.3.1 iSchools, Technologies, and Artificial Intelligence, ML, and Robotics 19.4 Artificial Intelligence, ML, and Robotics:An Overview 19.5 Artificial Intelligence, ML, and Roboticsas an Academic Program: A Case on iSchools—North American Region 19.6 Suggestions 19.7 Motivation and Future Works 19.8 Conclusion References Index 403 403 404 405 405 406 407 408 410 413 414 417 417 419 420 420 427 428 431 435 435 436 439 |
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| series2 | Advances in learning analytics for intelligent cloud-IOT systems |
| spelling | Machine learning techniques and analytics for cloud security edited by Rajdeep Chakraborty, Anupam Ghosh and Jyotsna Kumar Mandal [S.l.] John Wiley 2022 xxiii, 443 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Advances in learning analytics for intelligent cloud-IOT systems Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Computersicherheit (DE-588)4274324-2 gnd rswk-swf Cloud Computing (DE-588)7623494-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Cloud Computing (DE-588)7623494-0 s Computersicherheit (DE-588)4274324-2 s DE-604 Chakraborty, Rajdeep Sonstige (DE-588)1254081291 oth Ghosh, Anupam ca. 20./21. Jh. Sonstige (DE-588)1260593932 oth Mandal, J. K. 1960- Sonstige (DE-588)1186655399 oth Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033585904&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Machine learning techniques and analytics for cloud security Maschinelles Lernen (DE-588)4193754-5 gnd Computersicherheit (DE-588)4274324-2 gnd Cloud Computing (DE-588)7623494-0 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4274324-2 (DE-588)7623494-0 |
| title | Machine learning techniques and analytics for cloud security |
| title_auth | Machine learning techniques and analytics for cloud security |
| title_exact_search | Machine learning techniques and analytics for cloud security |
| title_exact_search_txtP | Machine learning techniques and analytics for cloud security |
| title_full | Machine learning techniques and analytics for cloud security edited by Rajdeep Chakraborty, Anupam Ghosh and Jyotsna Kumar Mandal |
| title_fullStr | Machine learning techniques and analytics for cloud security edited by Rajdeep Chakraborty, Anupam Ghosh and Jyotsna Kumar Mandal |
| title_full_unstemmed | Machine learning techniques and analytics for cloud security edited by Rajdeep Chakraborty, Anupam Ghosh and Jyotsna Kumar Mandal |
| title_short | Machine learning techniques and analytics for cloud security |
| title_sort | machine learning techniques and analytics for cloud security |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Computersicherheit (DE-588)4274324-2 gnd Cloud Computing (DE-588)7623494-0 gnd |
| topic_facet | Maschinelles Lernen Computersicherheit Cloud Computing |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033585904&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT chakrabortyrajdeep machinelearningtechniquesandanalyticsforcloudsecurity AT ghoshanupam machinelearningtechniquesandanalyticsforcloudsecurity AT mandaljk machinelearningtechniquesandanalyticsforcloudsecurity |