{"product_id":"machine-learning-techniques-and-analytics-for-cloud-security-9781119762256","title":"Machine Learning Techniques and Analytics for","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY 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 co\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eContents\u003c\/p\u003e \u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Conceptual Aspects on Cloud and Applications of Machine Learning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Hybrid Cloud: A New Paradigm in Cloud Computing 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMoumita Deb and Abantika Choudhury\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Hybrid Cloud 5\u003c\/p\u003e \u003cp\u003e1.2.1 Architecture 6\u003c\/p\u003e \u003cp\u003e1.2.2 Why Hybrid Cloud is Required? 6\u003c\/p\u003e \u003cp\u003e1.2.3 Business and Hybrid Cloud 7\u003c\/p\u003e \u003cp\u003e1.2.4 Things to Remember When Deploying Hybrid Cloud 8\u003c\/p\u003e \u003cp\u003e1.3 Comparison Among Different Hybrid Cloud Providers 9\u003c\/p\u003e \u003cp\u003e1.3.1 Cloud Storage and Backup Benefits 11\u003c\/p\u003e \u003cp\u003e1.3.2 Pros and Cons of Different Service Providers 11\u003c\/p\u003e \u003cp\u003e1.3.2.1 AWS Outpost 12\u003c\/p\u003e \u003cp\u003e1.3.2.2 Microsoft Azure Stack 12\u003c\/p\u003e \u003cp\u003e1.3.2.3 Google Cloud Anthos 12\u003c\/p\u003e \u003cp\u003e1.3.3 Review on Storage of the Providers 13\u003c\/p\u003e \u003cp\u003e1.3.3.1 AWS Outpost Storage 13\u003c\/p\u003e \u003cp\u003e1.3.3.2 Google Cloud Anthos Storage 13\u003c\/p\u003e \u003cp\u003e1.3.4 Pricing 15\u003c\/p\u003e \u003cp\u003e1.4 Hybrid Cloud in Education 15\u003c\/p\u003e \u003cp\u003e1.5 Significance of Hybrid Cloud Post-Pandemic 15\u003c\/p\u003e \u003cp\u003e1.6 Security in Hybrid Cloud 16\u003c\/p\u003e \u003cp\u003e1.6.1 Role of Human Error in Cloud Security 18\u003c\/p\u003e \u003cp\u003e1.6.2 Handling Security Challenges 18\u003c\/p\u003e \u003cp\u003e1.7 Use of AI in Hybrid Cloud 19\u003c\/p\u003e \u003cp\u003e1.8 Future Research Direction 21\u003c\/p\u003e \u003cp\u003e1.9 Conclusion 22\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003exix\u003c\/p\u003e \u003cp\u003ev\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework 25\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShillpi Mishrra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 25\u003c\/p\u003e \u003cp\u003e2.2 Proposed Methodology 27\u003c\/p\u003e \u003cp\u003e2.3 Result 28\u003c\/p\u003e \u003cp\u003e2.3.1 Description of Datasets 29\u003c\/p\u003e \u003cp\u003e2.3.2 Analysis of Result 29\u003c\/p\u003e \u003cp\u003e2.3.3 Validation of Results 31\u003c\/p\u003e \u003cp\u003e2.3.3.1 T-Test (Statistical Validation) 31\u003c\/p\u003e \u003cp\u003e2.3.3.2 Statistical Validation 33\u003c\/p\u003e \u003cp\u003e2.3.4 Glycan Cloud 37\u003c\/p\u003e \u003cp\u003e2.4 Conclusions and Future Work 38\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) 41\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSubir Hazra, Alia Nikhat Khurshid and Akriti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.2 Related Methods 44\u003c\/p\u003e \u003cp\u003e3.3 Methodology 46\u003c\/p\u003e \u003cp\u003e3.3.1 Description 47\u003c\/p\u003e \u003cp\u003e3.3.2 Flowchart 49\u003c\/p\u003e \u003cp\u003e3.3.3 Algorithm 49\u003c\/p\u003e \u003cp\u003e3.3.4 Interpretation of the Algorithm 50\u003c\/p\u003e \u003cp\u003e3.3.5 Illustration 50\u003c\/p\u003e \u003cp\u003e3.4 Result 51\u003c\/p\u003e \u003cp\u003e3.4.1 Description of the Dataset 51\u003c\/p\u003e \u003cp\u003e3.4.2 Result Analysis 51\u003c\/p\u003e \u003cp\u003e3.4.3 Result Set Validation 52\u003c\/p\u003e \u003cp\u003e3.5 Application in Cloud Domain 56\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 58\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Cloud Security Systems Using Machine Learning Techniques 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology 63\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSoumen Santra, Partha Mukherjee and Arpan Deyasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 64\u003c\/p\u003e \u003cp\u003e4.2 Home Automation System 65\u003c\/p\u003e \u003cp\u003e4.2.1 Sensors 65\u003c\/p\u003e \u003cp\u003e4.2.2 Protocols 66\u003c\/p\u003e \u003cp\u003e4.2.3 Technologies 66\u003c\/p\u003e \u003cp\u003e4.2.4 Advantages 67\u003c\/p\u003e \u003cp\u003e4.2.5 Disadvantages 67\u003c\/p\u003e \u003cp\u003e4.3 Literature Review 67\u003c\/p\u003e \u003cp\u003e4.4 Role of Sensors and Microcontrollers in Smart Home Design 68\u003c\/p\u003e \u003cp\u003e4.5 Motivation of the Project 70\u003c\/p\u003e \u003cp\u003e4.6 Smart Informative and Command Accepting Interface 70\u003c\/p\u003e \u003cp\u003e4.7 Data Flow Diagram 71\u003c\/p\u003e \u003cp\u003e4.8 Components of Informative Interface 72\u003c\/p\u003e \u003cp\u003e4.9 Results 73\u003c\/p\u003e \u003cp\u003e4.9.1 Circuit Design 73\u003c\/p\u003e \u003cp\u003e4.9.2 LDR Data 76\u003c\/p\u003e \u003cp\u003e4.9.3 API Data 76\u003c\/p\u003e \u003cp\u003e4.10 Conclusion 78\u003c\/p\u003e \u003cp\u003e4.11 Future Scope 78\u003c\/p\u003e \u003cp\u003eReferences 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security 81\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnirban Bhowmik, Sunil Karforma and Joydeep Dey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 81\u003c\/p\u003e \u003cp\u003e5.2 Literature Review 85\u003c\/p\u003e \u003cp\u003e5.3 The Problem 86\u003c\/p\u003e \u003cp\u003e5.4 Objectives and Contributions 86\u003c\/p\u003e \u003cp\u003e5.5 Methodology 87\u003c\/p\u003e \u003cp\u003e5.6 Results and Discussions 91\u003c\/p\u003e \u003cp\u003e5.6.1 Statistical Analysis 93\u003c\/p\u003e \u003cp\u003e5.6.2 Randomness Test of Key 94\u003c\/p\u003e \u003cp\u003e5.6.3 Key Sensitivity Analysis 95\u003c\/p\u003e \u003cp\u003e5.6.4 Security Analysis 96\u003c\/p\u003e \u003cp\u003e5.6.5 Dataset Used on ANN 96\u003c\/p\u003e \u003cp\u003e5.6.6 Comparisons 98\u003c\/p\u003e \u003cp\u003e5.7 Conclusions 99\u003c\/p\u003e \u003cp\u003eReferences 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 An Efficient Intrusion Detection System on Various Datasets Using Machine Learning Techniques 103\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDebraj Chatterjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 103\u003c\/p\u003e \u003cp\u003e6.2 Motivation and Justification of the Proposed Work 104\u003c\/p\u003e \u003cp\u003e6.3 Terminology Related to IDS 105\u003c\/p\u003e \u003cp\u003e6.3.1 Network 105\u003c\/p\u003e \u003cp\u003e6.3.2 Network Traffic 105\u003c\/p\u003e \u003cp\u003e6.3.3 Intrusion 106\u003c\/p\u003e \u003cp\u003e6.3.4 Intrusion Detection System 106\u003c\/p\u003e \u003cp\u003e6.3.4.1 Various Types of IDS 108\u003c\/p\u003e \u003cp\u003e6.3.4.2 Working Methodology of IDS 108\u003c\/p\u003e \u003cp\u003e6.3.4.3 Characteristics of IDS 109\u003c\/p\u003e \u003cp\u003e6.3.4.4 Advantages of IDS 110\u003c\/p\u003e \u003cp\u003e6.3.4.5 Disadvantages of IDS 111\u003c\/p\u003e \u003cp\u003e6.3.5 Intrusion Prevention System (IPS) 111\u003c\/p\u003e \u003cp\u003e6.3.5.1 Network-Based Intrusion Prevention System (NIPS) 111\u003c\/p\u003e \u003cp\u003e6.3.5.2 Wireless Intrusion Prevention System (WIPS) 112\u003c\/p\u003e \u003cp\u003e6.3.5.3 Network Behavior Analysis (NBA) 112\u003c\/p\u003e \u003cp\u003e6.3.5.4 Host-Based Intrusion Prevention System (HIPS) 112\u003c\/p\u003e \u003cp\u003e6.3.6 Comparison of IPS With IDS\/Relation Between IDS and IPS 112\u003c\/p\u003e \u003cp\u003e6.3.7 Different Methods of Evasion in Networks 113\u003c\/p\u003e \u003cp\u003e6.4 Intrusion Attacks on Cloud Environment 114\u003c\/p\u003e \u003cp\u003e6.5 Comparative Studies 116\u003c\/p\u003e \u003cp\u003e6.6 Proposed Methodology 121\u003c\/p\u003e \u003cp\u003e6.7 Result 122\u003c\/p\u003e \u003cp\u003e6.8 Conclusion and Future Scope 125\u003c\/p\u003e \u003cp\u003eReferences 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 You Are Known by Your Mood: A Text-Based Sentiment Analysis for Cloud Security 129\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbhijit Roy and Parthajit Roy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 129\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 131\u003c\/p\u003e \u003cp\u003e7.3 Essential Prerequisites 133\u003c\/p\u003e \u003cp\u003e7.3.1 Security Aspects 133\u003c\/p\u003e \u003cp\u003e7.3.2 Machine Learning Tools 135\u003c\/p\u003e \u003cp\u003e7.3.2.1 Naïve Bayes Classifier 135\u003c\/p\u003e \u003cp\u003e7.3.2.2 Artificial Neural Network 136\u003c\/p\u003e \u003cp\u003e7.4 Proposed Model 136\u003c\/p\u003e \u003cp\u003e7.5 Experimental Setup 138\u003c\/p\u003e \u003cp\u003e7.6 Results and Discussions 139\u003c\/p\u003e \u003cp\u003e7.7 Application in Cloud Security 142\u003c\/p\u003e \u003cp\u003e7.7.1 Ask an Intelligent Security Question 142\u003c\/p\u003e \u003cp\u003e7.7.2 Homomorphic Data Storage 142\u003c\/p\u003e \u003cp\u003e7.7.3 Information Diffusion 144\u003c\/p\u003e \u003cp\u003e7.8 Conclusion and Future Scope 144\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The State-of-the-Art in Zero-Knowledge Authentication Proof for Cloud 149\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriyanka Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 149\u003c\/p\u003e \u003cp\u003e8.2 Attacks and Countermeasures 153\u003c\/p\u003e \u003cp\u003e8.2.1 Malware and Ransomware Breaches 154\u003c\/p\u003e \u003cp\u003e8.2.2 Prevention of Distributing Denial of Service 154\u003c\/p\u003e \u003cp\u003e8.2.3 Threat Detection 154\u003c\/p\u003e \u003cp\u003e8.3 Zero-Knowledge Proof 154\u003c\/p\u003e \u003cp\u003e8.4 Machine Learning for Cloud Computing 156\u003c\/p\u003e \u003cp\u003e8.4.1 Types of Learning Algorithms 156\u003c\/p\u003e \u003cp\u003e8.4.1.1 Supervised Learning 156\u003c\/p\u003e \u003cp\u003e8.4.1.2 Supervised Learning Approach 156\u003c\/p\u003e \u003cp\u003e8.4.1.3 Unsupervised Learning 157\u003c\/p\u003e \u003cp\u003e8.4.2 Application on Machine Learning for Cloud Computing 157\u003c\/p\u003e \u003cp\u003e8.4.2.1 Image Recognition 157\u003c\/p\u003e \u003cp\u003e8.4.2.2 Speech Recognition 157\u003c\/p\u003e \u003cp\u003e8.4.2.3 Medical Diagnosis 158\u003c\/p\u003e \u003cp\u003e8.4.2.4 Learning Associations 158\u003c\/p\u003e \u003cp\u003e8.4.2.5 Classification 158\u003c\/p\u003e \u003cp\u003e8.4.2.6 Prediction 158\u003c\/p\u003e \u003cp\u003e8.4.2.7 Extraction 158\u003c\/p\u003e \u003cp\u003e8.4.2.8 Regression 158\u003c\/p\u003e \u003cp\u003e8.4.2.9 Financial Services 159\u003c\/p\u003e \u003cp\u003e8.5 Zero-Knowledge Proof: Details 159\u003c\/p\u003e \u003cp\u003e8.5.1 Comparative Study 159\u003c\/p\u003e \u003cp\u003e8.5.1.1 Fiat-Shamir ZKP Protocol 159\u003c\/p\u003e \u003cp\u003e8.5.2 Diffie-Hellman Key Exchange Algorithm 161\u003c\/p\u003e \u003cp\u003e8.5.2.1 Discrete Logarithm Attack 161\u003c\/p\u003e \u003cp\u003e8.5.2.2 Man-in-the-Middle Attack 162\u003c\/p\u003e \u003cp\u003e8.5.3 ZKP Version 1 162\u003c\/p\u003e \u003cp\u003e8.5.4 ZKP Version 2 162\u003c\/p\u003e \u003cp\u003e8.5.5 Analysis 164\u003c\/p\u003e \u003cp\u003e8.5.6 Cloud Security Architecture 166\u003c\/p\u003e \u003cp\u003e8.5.7 Existing Cloud Computing Architectures 167\u003c\/p\u003e \u003cp\u003e8.5.8 Issues With Current Clouds 167\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 168\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques 171\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmartya Chakraborty, Suvendu Chattaraj, Sangita Karmakar and Shillpi Mishrra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 171\u003c\/p\u003e \u003cp\u003e9.2 Literature Review 173\u003c\/p\u003e \u003cp\u003e9.3 Motivation 174\u003c\/p\u003e \u003cp\u003e9.4 System Overview 175\u003c\/p\u003e \u003cp\u003e9.5 Data Description 176\u003c\/p\u003e \u003cp\u003e9.6 Data Processing 176\u003c\/p\u003e \u003cp\u003e9.7 Feature Extraction 178\u003c\/p\u003e \u003cp\u003e9.8 Learning Techniques Used 179\u003c\/p\u003e \u003cp\u003e9.8.1 Support Vector Machine 179\u003c\/p\u003e \u003cp\u003e9.8.2 k-Nearest Neighbors 180\u003c\/p\u003e \u003cp\u003e9.8.3 Decision Tree 180\u003c\/p\u003e \u003cp\u003e9.8.4 Convolutional Neural Network 180\u003c\/p\u003e \u003cp\u003e9.9 Experimental Setup 182\u003c\/p\u003e \u003cp\u003e9.10 Evaluation Metrics 183\u003c\/p\u003e \u003cp\u003e9.11 Experimental Results 185\u003c\/p\u003e \u003cp\u003e9.11.1 Observations in Comparison With State-of-the-Art 187\u003c\/p\u003e \u003cp\u003e9.12 Application in Cloud Architecture 188\u003c\/p\u003e \u003cp\u003e9.13 Conclusion 189\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 An Intelligent System for Securing Network From Intrusion Detection and Prevention of Phishing Attack Using Machine Learning Approaches 193\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSumit Banik, Sagar Banik and Anupam Mukherjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 193\u003c\/p\u003e \u003cp\u003e10.1.1 Types of Phishing 195\u003c\/p\u003e \u003cp\u003e10.1.1.1 Spear Phishing 195\u003c\/p\u003e \u003cp\u003e10.1.1.2 Whaling 195\u003c\/p\u003e \u003cp\u003e10.1.1.3 Catphishing and Catfishing 195\u003c\/p\u003e \u003cp\u003e10.1.1.4 Clone Phishing 196\u003c\/p\u003e \u003cp\u003e10.1.1.5 Voice Phishing 196\u003c\/p\u003e \u003cp\u003e10.1.2 Techniques of Phishing 196\u003c\/p\u003e \u003cp\u003e10.1.2.1 Link Manipulation 196\u003c\/p\u003e \u003cp\u003e10.1.2.2 Filter Evasion 196\u003c\/p\u003e \u003cp\u003e10.1.2.3 Website Forgery 196\u003c\/p\u003e \u003cp\u003e10.1.2.4 Covert Redirect 197\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 197\u003c\/p\u003e \u003cp\u003e10.3 Materials and Methods 199\u003c\/p\u003e \u003cp\u003e10.3.1 Dataset and Attributes 199\u003c\/p\u003e \u003cp\u003e10.3.2 Proposed Methodology 199\u003c\/p\u003e \u003cp\u003e10.3.2.1 Logistic Regression 202\u003c\/p\u003e \u003cp\u003e10.3.2.2 Naïve Bayes 202\u003c\/p\u003e \u003cp\u003e10.3.2.3 Support Vector Machine 203\u003c\/p\u003e \u003cp\u003e10.3.2.4 Voting Classification 203\u003c\/p\u003e \u003cp\u003e10.4 Result Analysis 204\u003c\/p\u003e \u003cp\u003e10.4.1 Analysis of Different Parameters for ML Models 204\u003c\/p\u003e \u003cp\u003e10.4.2 Predictive Outcome Analysis in Phishing URLs Dataset 205\u003c\/p\u003e \u003cp\u003e10.4.3 Analysis of Performance Metrics 206\u003c\/p\u003e \u003cp\u003e10.4.4 Statistical Analysis of Results 210\u003c\/p\u003e \u003cp\u003e‌0.4.4. 1 ANOVA: Two-Factor Without Replication 210\u003c\/p\u003e \u003cp\u003e10.4.4.2 ANOVA: Single Factor 210\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 210\u003c\/p\u003e \u003cp\u003eReferences 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Cloud Security Analysis Using Machine Learning Techniques 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Cloud Security Using Honeypot Network and Blockchain: A Review 215\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSmarta Sangui * and Swarup Kr Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 215\u003c\/p\u003e \u003cp\u003e11.2 Cloud Computing Overview 216\u003c\/p\u003e \u003cp\u003e11.2.1 Types of Cloud Computing Services 216\u003c\/p\u003e \u003cp\u003e11.2.1.1 Software as a Service 216\u003c\/p\u003e \u003cp\u003e11.2.1.2 Infrastructure as a Service 218\u003c\/p\u003e \u003cp\u003e11.2.1.3 Platform as a Service 218\u003c\/p\u003e \u003cp\u003e11.2.2 Deployment Models of Cloud Computing 218\u003c\/p\u003e \u003cp\u003e11.2.2.1 Public Cloud 218\u003c\/p\u003e \u003cp\u003e11.2.2.2 Private Cloud 218\u003c\/p\u003e \u003cp\u003e11.2.2.3 Community Cloud 219\u003c\/p\u003e \u003cp\u003e11.2.2.4 Hybrid Cloud 219\u003c\/p\u003e \u003cp\u003e11.2.3 Security Concerns in Cloud Computing 219\u003c\/p\u003e \u003cp\u003e11.2.3.1 Data Breaches 219\u003c\/p\u003e \u003cp\u003e11.2.3.2 Insufficient Change Control and Misconfiguration 219\u003c\/p\u003e \u003cp\u003e11.2.3.3 Lack of Strategy and Security Architecture 220\u003c\/p\u003e \u003cp\u003e11.2.3.4 Insufficient Identity, Credential, Access, and Key Management 220\u003c\/p\u003e \u003cp\u003e11.2.3.5 Account Hijacking 220\u003c\/p\u003e \u003cp\u003e11.2.3.6 Insider Threat 220\u003c\/p\u003e \u003cp\u003e11.2.3.7 Insecure Interfaces and APIs 220\u003c\/p\u003e \u003cp\u003e11.2.3.8 Weak Control Plane 221\u003c\/p\u003e \u003cp\u003e11.3 Honeypot System 221\u003c\/p\u003e \u003cp\u003e11.3.1 VM (Virtual Machine) as Honeypot in the Cloud 221\u003c\/p\u003e \u003cp\u003e11.3.2 Attack Sensing and Analyzing Framework 222\u003c\/p\u003e \u003cp\u003e11.3.3 A Fuzzy Technique Against Fingerprinting Attacks 223\u003c\/p\u003e \u003cp\u003e11.3.4 Detecting and Classifying Malicious Access 224\u003c\/p\u003e \u003cp\u003e11.3.5 A Bayesian Defense Model for Deceptive Attack 224\u003c\/p\u003e \u003cp\u003e11.3.6 Strategic Game Model for DDoS Attacks in Smart Grid 226\u003c\/p\u003e \u003cp\u003e11.4 Blockchain 227\u003c\/p\u003e \u003cp\u003e11.4.1 Blockchain-Based Encrypted Cloud Storage 228\u003c\/p\u003e \u003cp\u003e11.4.2 Cloud-Assisted EHR Sharing via Consortium Blockchain 229\u003c\/p\u003e \u003cp\u003e11.4.3 Blockchain-Secured Cloud Storage 230\u003c\/p\u003e \u003cp\u003e11.4.4 Blockchain and Edge Computing–Based Security Architecture 230\u003c\/p\u003e \u003cp\u003e11.4.5 Data Provenance Architecture in Cloud Ecosystem Using Blockchain 231\u003c\/p\u003e \u003cp\u003e11.6 Comparative Analysis 233\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 233\u003c\/p\u003e \u003cp\u003eReferences 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Machine Learning–Based Security in Cloud Database—A Survey 239\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eUtsav Vora, Jayleena Mahato, Hrishav Dasgupta, Anand Kumar and Swarup Kr Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 239\u003c\/p\u003e \u003cp\u003e12.2 Security Threats and Attacks 241\u003c\/p\u003e \u003cp\u003e12.3 Dataset Description 244\u003c\/p\u003e \u003cp\u003e12.3.1 NSL-KDD Dataset 244\u003c\/p\u003e \u003cp\u003e12.3.2 UNSW-NB15 Dataset 244\u003c\/p\u003e \u003cp\u003e12.4 Machine Learning for Cloud Security 245\u003c\/p\u003e \u003cp\u003e12.4.1 Supervised Learning Techniques 245\u003c\/p\u003e \u003cp\u003e12.4.1.1 Support Vector Machine 245\u003c\/p\u003e \u003cp\u003e12.4.1.2 Artificial Neural Network 247\u003c\/p\u003e \u003cp\u003e12.4.1.3 Deep Learning 249\u003c\/p\u003e \u003cp\u003e12.4.1.4 Random Forest 250\u003c\/p\u003e \u003cp\u003e12.4.2 Unsupervised Learning Techniques 251\u003c\/p\u003e \u003cp\u003e12.4.2.1 K-Means Clustering 252\u003c\/p\u003e \u003cp\u003e12.4.2.2 Fuzzy C-Means Clustering 253\u003c\/p\u003e \u003cp\u003e12.4.2.3 Expectation-Maximization Clustering 253\u003c\/p\u003e \u003cp\u003e12.4.2.4 Cuckoo Search With Particle Swarm Optimization (PSO) 254\u003c\/p\u003e \u003cp\u003e12.4.3 Hybrid Learning Techniques 256\u003c\/p\u003e \u003cp\u003e12.4.3.1 HIDCC: Hybrid Intrusion Detection Approach in Cloud Computing 256\u003c\/p\u003e \u003cp\u003e12.4.3.2 Clustering-Based Hybrid Model in Deep Learning Framework 257\u003c\/p\u003e \u003cp\u003e12.4.3.3 K-Nearest Neighbor–Based Fuzzy C-Means Mechanism 258\u003c\/p\u003e \u003cp\u003e12.4.3.4 K-Means Clustering Using Support Vector Machine 260\u003c\/p\u003e \u003cp\u003e12.4.3.5 K-Nearest Neighbor–Based Artificial Neural Network Mechanism 260\u003c\/p\u003e \u003cp\u003e12.4.3.6 Artificial Neural Network Fused With Support Vector Machine 261\u003c\/p\u003e \u003cp\u003e12.4.3.7 Particle Swarm Optimization–Based Probabilistic Neural Network 261\u003c\/p\u003e \u003cp\u003e12.5 Comparative Analysis 262\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 264\u003c\/p\u003e \u003cp\u003eReferences 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Machine Learning Adversarial Attacks: A Survey Beyond 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChandni Magoo and Puneet Garg\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 271\u003c\/p\u003e \u003cp\u003e13.2 Adversarial Learning 272\u003c\/p\u003e \u003cp\u003e13.2.1 Concept 272\u003c\/p\u003e \u003cp\u003e13.3 Taxonomy of Adversarial Attacks 273\u003c\/p\u003e \u003cp\u003e13.3.1 Attacks Based on Knowledge 273\u003c\/p\u003e \u003cp\u003e13.3.1.1 Black Box Attack (Transferable Attack) 273\u003c\/p\u003e \u003cp\u003e13.3.1.2 White Box Attack 274\u003c\/p\u003e \u003cp\u003e13.3.2 Attacks Based on Goals 275\u003c\/p\u003e \u003cp\u003e13.3.2.1 Target Attacks 275\u003c\/p\u003e \u003cp\u003e13.3.2.2 Non-Target Attacks 275\u003c\/p\u003e \u003cp\u003e13.3.3 Attacks Based on Strategies 275\u003c\/p\u003e \u003cp\u003e13.3.3.1 Poisoning Attacks 275\u003c\/p\u003e \u003cp\u003e13.3.3.2 Evasion Attacks 276\u003c\/p\u003e \u003cp\u003e13.3.4 Textual-Based Attacks (NLP) 276\u003c\/p\u003e \u003cp\u003e13.3.4.1 Character Level Attacks 276\u003c\/p\u003e \u003cp\u003e13.3.4.2 Word-Level Attacks 276\u003c\/p\u003e \u003cp\u003e13.3.4.3 Sentence-Level Attacks 276\u003c\/p\u003e \u003cp\u003e13.4 Review of Adversarial Attack Methods 276\u003c\/p\u003e \u003cp\u003e13.4.1 L-bfgs 277\u003c\/p\u003e \u003cp\u003e13.4.2 Feedforward Derivation Attack (Jacobian Attack) 277\u003c\/p\u003e \u003cp\u003e13.4.3 Fast Gradient Sign Method 278\u003c\/p\u003e \u003cp\u003e13.4.4 Methods of Different Text-Based Adversarial Attacks 278\u003c\/p\u003e \u003cp\u003e13.4.5 Adversarial Attacks Methods Based on Language Models 284\u003c\/p\u003e \u003cp\u003e13.4.6 Adversarial Attacks on Recommender Systems 284\u003c\/p\u003e \u003cp\u003e13.4.6.1 Random Attack 284\u003c\/p\u003e \u003cp\u003e13.4.6.2 Average Attack 286\u003c\/p\u003e \u003cp\u003e13.4.6.3 Bandwagon Attack 286\u003c\/p\u003e \u003cp\u003e13.4.6.4 Reverse Bandwagon Attack 286\u003c\/p\u003e \u003cp\u003e13.5 Adversarial Attacks on Cloud-Based Platforms 287\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 288\u003c\/p\u003e \u003cp\u003eReferences 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Protocols for Cloud Security 293\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eWeijing You and Bo Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 293\u003c\/p\u003e \u003cp\u003e14.2 System and Adversarial Model 295\u003c\/p\u003e \u003cp\u003e14.2.1 System Model 295\u003c\/p\u003e \u003cp\u003e14.2.2 Adversarial Model 295\u003c\/p\u003e \u003cp\u003e14.3 Protocols for Data Protection in Secure Cloud Computing 296\u003c\/p\u003e \u003cp\u003e14.3.1 Homomorphic Encryption 297\u003c\/p\u003e \u003cp\u003e14.3.2 Searchable Encryption 298\u003c\/p\u003e \u003cp\u003e14.3.3 Attribute-Based Encryption 299\u003c\/p\u003e \u003cp\u003e14.3.4 Secure Multi-Party Computation 300\u003c\/p\u003e \u003cp\u003e14.4 Protocols for Data Protection in Secure Cloud Storage 301\u003c\/p\u003e \u003cp\u003e14.4.1 Proofs of Encryption 301\u003c\/p\u003e \u003cp\u003e14.4.2 Secure Message-Locked Encryption 303\u003c\/p\u003e \u003cp\u003e14.4.3 Proofs of Storage 303\u003c\/p\u003e \u003cp\u003e14.4.4 Proofs of Ownership 305\u003c\/p\u003e \u003cp\u003e14.4.5 Proofs of Reliability 306\u003c\/p\u003e \u003cp\u003e14.5 Protocols for Secure Cloud Systems 309\u003c\/p\u003e \u003cp\u003e14.6 Protocols for Cloud Security in the Future 309\u003c\/p\u003e \u003cp\u003e14.7 Conclusion 310\u003c\/p\u003e \u003cp\u003eReferences 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Case Studies Focused on Cloud Security 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 A Study on Google Cloud Platform (GCP) and Its Security 315\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAgniswar Roy, Abhik Banerjee and Navneet Bhardwaj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 315\u003c\/p\u003e \u003cp\u003e15.1.1 Google Cloud Platform Current Market Holding 316\u003c\/p\u003e \u003cp\u003e15.1.1.1 The Forrester Wave 317\u003c\/p\u003e \u003cp\u003e15.1.1.2 Gartner Magic Quadrant 317\u003c\/p\u003e \u003cp\u003e15.1.2 Google Cloud Platform Work Distribution 317\u003c\/p\u003e \u003cp\u003e15.1.2.1 SaaS 318\u003c\/p\u003e \u003cp\u003e15.1.2.2 PaaS 318\u003c\/p\u003e \u003cp\u003e15.1.2.3 IaaS 318\u003c\/p\u003e \u003cp\u003e15.1.2.4 On-Premise 318\u003c\/p\u003e \u003cp\u003e15.2 Google Cloud Platform’s Security Features Basic Overview 318\u003c\/p\u003e \u003cp\u003e15.2.1 Physical Premises Security 319\u003c\/p\u003e \u003cp\u003e15.2.2 Hardware Security 319\u003c\/p\u003e \u003cp\u003e15.2.3 Inter-Service Security 319\u003c\/p\u003e \u003cp\u003e15.2.4 Data Security 320\u003c\/p\u003e \u003cp\u003e15.2.5 Internet Security 320\u003c\/p\u003e \u003cp\u003e15.2.6 In-Software Security 320\u003c\/p\u003e \u003cp\u003e15.2.7 End User Access Security 321\u003c\/p\u003e \u003cp\u003e15.3 Google Cloud Platform’s Architecture 321\u003c\/p\u003e \u003cp\u003e15.3.1 Geographic Zone 321\u003c\/p\u003e \u003cp\u003e15.3.2 Resource Management 322\u003c\/p\u003e \u003cp\u003e15.3.2.1 Iam 322\u003c\/p\u003e \u003cp\u003e15.3.2.2 Roles 323\u003c\/p\u003e \u003cp\u003e15.3.2.3 Billing 323\u003c\/p\u003e \u003cp\u003e15.4 Key Security Features 324\u003c\/p\u003e \u003cp\u003e15.4.1 Iap 324\u003c\/p\u003e \u003cp\u003e15.4.2 Compliance 325\u003c\/p\u003e \u003cp\u003e15.4.3 Policy Analyzer 326\u003c\/p\u003e \u003cp\u003e15.4.4 Security Command Center 326\u003c\/p\u003e \u003cp\u003e15.4.4.1 Standard Tier 326\u003c\/p\u003e \u003cp\u003e15.4.4.2 Premium Tier 326\u003c\/p\u003e \u003cp\u003e15.4.5 Data Loss Protection 329\u003c\/p\u003e \u003cp\u003e15.4.6 Key Management 329\u003c\/p\u003e \u003cp\u003e15.4.7 Secret Manager 330\u003c\/p\u003e \u003cp\u003e15.4.8 Monitoring 330\u003c\/p\u003e \u003cp\u003e15.5 Key Application Features 330\u003c\/p\u003e \u003cp\u003e15.5.1 Stackdriver (Currently Operations) 330\u003c\/p\u003e \u003cp\u003e15.5.1.1 Profiler 330\u003c\/p\u003e \u003cp\u003e15.5.1.2 Cloud Debugger 330\u003c\/p\u003e \u003cp\u003e15.5.1.3 Trace 331\u003c\/p\u003e \u003cp\u003e15.5.2 Network 331\u003c\/p\u003e \u003cp\u003e15.5.3 Virtual Machine Specifications 332\u003c\/p\u003e \u003cp\u003e15.5.4 Preemptible VMs 332\u003c\/p\u003e \u003cp\u003e15.6 Computation in Google Cloud Platform 332\u003c\/p\u003e \u003cp\u003e15.6.1 Compute Engine 332\u003c\/p\u003e \u003cp\u003e15.6.2 App Engine 333\u003c\/p\u003e \u003cp\u003e15.6.3 Container Engine 333\u003c\/p\u003e \u003cp\u003e15.6.4 Cloud Functions 333\u003c\/p\u003e \u003cp\u003e15.7 Storage in Google Cloud Platform 333\u003c\/p\u003e \u003cp\u003e15.8 Network in Google Cloud Platform 334\u003c\/p\u003e \u003cp\u003e15.9 Data in Google Cloud Platform 334\u003c\/p\u003e \u003cp\u003e15.10 Machine Learning in Google Cloud Platform 335\u003c\/p\u003e \u003cp\u003e15.11 Conclusion 335\u003c\/p\u003e \u003cp\u003eReferences 337\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Case Study of Azure and Azure Security Practices 339\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNavneet Bhardwaj, Abhik Banerjee and Agniswar Roy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 339\u003c\/p\u003e \u003cp\u003e16.1.1 Azure Current Market Holding 340\u003c\/p\u003e \u003cp\u003e16.1.2 The Forrester Wave 340\u003c\/p\u003e \u003cp\u003e16.1.3 Gartner Magic Quadrant 340\u003c\/p\u003e \u003cp\u003e16.2 Microsoft Azure—The Security Infrastructure 341\u003c\/p\u003e \u003cp\u003e16.2.1 Azure Security Features and Tools 341\u003c\/p\u003e \u003cp\u003e16.2.2 Network Security 342\u003c\/p\u003e \u003cp\u003e16.3 Data Encryption 342\u003c\/p\u003e \u003cp\u003e16.3.1 Data Encryption at Rest 342\u003c\/p\u003e \u003cp\u003e16.3.2 Data Encryption at Transit 342\u003c\/p\u003e \u003cp\u003e16.3.3 Asset and Inventory Management 343\u003c\/p\u003e \u003cp\u003e16.3.4 Azure Marketplace 343\u003c\/p\u003e \u003cp\u003e16.4 Azure Cloud Security Architecture 344\u003c\/p\u003e \u003cp\u003e16.4.1 Working 344\u003c\/p\u003e \u003cp\u003e16.4.2 Design Principles 344\u003c\/p\u003e \u003cp\u003e16.4.2.1 Alignment of Security Policies 344\u003c\/p\u003e \u003cp\u003e16.4.2.2 Building a Comprehensive Strategy 345\u003c\/p\u003e \u003cp\u003e16.4.2.3 Simplicity Driven 345\u003c\/p\u003e \u003cp\u003e16.4.2.4 Leveraging Native Controls 345\u003c\/p\u003e \u003cp\u003e16.4.2.5 Identification-Based Authentication 345\u003c\/p\u003e \u003cp\u003e16.4.2.6 Accountability 345\u003c\/p\u003e \u003cp\u003e16.4.2.7 Embracing Automation 345\u003c\/p\u003e \u003cp\u003e16.4.2.8 Stress on Information Protection 345\u003c\/p\u003e \u003cp\u003e16.4.2.9 Continuous Evaluation 346\u003c\/p\u003e \u003cp\u003e16.4.2.10 Skilled Workforce 346\u003c\/p\u003e \u003cp\u003e16.5 Azure Architecture 346\u003c\/p\u003e \u003cp\u003e16.5.1 Components 346\u003c\/p\u003e \u003cp\u003e16.5.1.1 Azure Api Gateway 346\u003c\/p\u003e \u003cp\u003e16.5.1.2 Azure Functions 346\u003c\/p\u003e \u003cp\u003e16.5.2 Services 347\u003c\/p\u003e \u003cp\u003e16.5.2.1 Azure Virtual Machine 347\u003c\/p\u003e \u003cp\u003e16.5.2.2 Blob Storage 347\u003c\/p\u003e \u003cp\u003e16.5.2.3 Azure Virtual Network 348\u003c\/p\u003e \u003cp\u003e16.5.2.4 Content Delivery Network 348\u003c\/p\u003e \u003cp\u003e16.5.2.5 Azure SQL Database 349\u003c\/p\u003e \u003cp\u003e16.6 Features of Azure 350\u003c\/p\u003e \u003cp\u003e16.6.1 Key Features 350\u003c\/p\u003e \u003cp\u003e16.6.1.1 Data Resiliency 350\u003c\/p\u003e \u003cp\u003e16.6.1.2 Data Security 350\u003c\/p\u003e \u003cp\u003e16.6.1.3 BCDR Integration 350\u003c\/p\u003e \u003cp\u003e16.6.1.4 Storage Management 351\u003c\/p\u003e \u003cp\u003e16.6.1.5 Single Pane View 351\u003c\/p\u003e \u003cp\u003e16.7 Common Azure Security Features 351\u003c\/p\u003e \u003cp\u003e16.7.1 Security Center 351\u003c\/p\u003e \u003cp\u003e16.7.2 Key Vault 351\u003c\/p\u003e \u003cp\u003e16.7.3 Azure Active Directory 352\u003c\/p\u003e \u003cp\u003e16.7.3.1 Application Management 352\u003c\/p\u003e \u003cp\u003e16.7.3.2 Conditional Access 352\u003c\/p\u003e \u003cp\u003e16.7.3.3 Device Identity Management 352\u003c\/p\u003e \u003cp\u003e​16.7.3. 4 Identity Protection 353\u003c\/p\u003e \u003cp\u003e16.7.3.5 Azure Sentinel 353\u003c\/p\u003e \u003cp\u003e16.7.3.6 Privileged Identity Management 354\u003c\/p\u003e \u003cp\u003e16.7.3.7 Multifactor Authentication 354\u003c\/p\u003e \u003cp\u003e16.7.3.8 Single Sign On 354\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 355\u003c\/p\u003e \u003cp\u003eReferences 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Nutanix Hybrid Cloud From Security Perspective 357\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbhik Banerjee, Agniswar Roy, Amar Kalvikatte and Navneet Bhardwaj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 357\u003c\/p\u003e \u003cp\u003e17.2 Growth of Nutanix 358\u003c\/p\u003e \u003cp\u003e17.2.1 Gartner Magic Quadrant 358\u003c\/p\u003e \u003cp\u003e17.2.2 The Forrester Wave 358\u003c\/p\u003e \u003cp\u003e17.2.3 Consumer Acquisition 359\u003c\/p\u003e \u003cp\u003e17.2.4 Revenue 359\u003c\/p\u003e \u003cp\u003e17.3 Introductory Concepts 361\u003c\/p\u003e \u003cp\u003e17.3.1 Plane Concepts 361\u003c\/p\u003e \u003cp\u003e17.3.1.1 Control Plane 361\u003c\/p\u003e \u003cp\u003e17.3.1.2 Data Plane 361\u003c\/p\u003e \u003cp\u003e17.3.2 Security Technical Implementation Guides 362\u003c\/p\u003e \u003cp\u003e17.3.3 SaltStack and SCMA 362\u003c\/p\u003e \u003cp\u003e17.4 Nutanix Hybrid Cloud 362\u003c\/p\u003e \u003cp\u003e17.4.1 Prism 362\u003c\/p\u003e \u003cp\u003e17.4.1.1 Prism Element 363\u003c\/p\u003e \u003cp\u003e17.4.1.2 Prism Central 364\u003c\/p\u003e \u003cp\u003e17.4.2 Acropolis 365\u003c\/p\u003e \u003cp\u003e17.4.2.1 Distributed Storage Fabric 365\u003c\/p\u003e \u003cp\u003e17.4.2.2 Ahv 367\u003c\/p\u003e \u003cp\u003e17.5 Reinforcing AHV and Controller VM 367\u003c\/p\u003e \u003cp\u003e17.6 Disaster Management and Recovery 368\u003c\/p\u003e \u003cp\u003e17.6.1 Protection Domains and Consistent Groups 368\u003c\/p\u003e \u003cp\u003e17.6.2 Nutanix DSF Replication of OpLog 369\u003c\/p\u003e \u003cp\u003e17.6.3 DSF Snapshots and VmQueisced Snapshot Service 370\u003c\/p\u003e \u003cp\u003e17.6.4 Nutanix Cerebro 370\u003c\/p\u003e \u003cp\u003e17.7 Security and Policy Management on Nutanix Hybrid Cloud 371\u003c\/p\u003e \u003cp\u003e17.7.1 Authentication on Nutanix 372\u003c\/p\u003e \u003cp\u003e17.7.2 Nutanix Data Encryption 372\u003c\/p\u003e \u003cp\u003e17.7.3 Security Policy Management 373\u003c\/p\u003e \u003cp\u003e17.7.3.1 Enforcing a Policy 374\u003c\/p\u003e \u003cp\u003e17.7.3.2 Priority of a Policy 374\u003c\/p\u003e \u003cp\u003e17.7.3.3 Automated Enforcement 374\u003c\/p\u003e \u003cp\u003e17.8 Network Security and Log Management 374\u003c\/p\u003e \u003cp\u003e17.8.1 Segmented and Unsegmented Network 375\u003c\/p\u003e \u003cp\u003e17.9 Conclusion 376\u003c\/p\u003e \u003cp\u003eReferences 376\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: Policy Aspects 379\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 A Data Science Approach Based on User Interactions to Generate Access Control Policies for Large Collections of Documents 381\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJedidiah Yanez-Sierra, Arturo Diaz-Perez and Victor Sosa-Sosa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 381\u003c\/p\u003e \u003cp\u003e18.2 Related Work 383\u003c\/p\u003e \u003cp\u003e18.3 Network Science Theory 384\u003c\/p\u003e \u003cp\u003e18.4 Approach to Spread Policies Using Networks Science 387\u003c\/p\u003e \u003cp\u003e18.4.1 Finding the Most Relevant Spreaders 388\u003c\/p\u003e \u003cp\u003e18.4.1.1 Weighting Users 389\u003c\/p\u003e \u003cp\u003e18.4.1.2 Selecting the Top � Spreaders 390\u003c\/p\u003e \u003cp\u003e18.4.2 Assign and Spread the Access Control Policies 390\u003c\/p\u003e \u003cp\u003e18.4.2.1 Access Control Policies 391\u003c\/p\u003e \u003cp\u003e18.4.2.2 Horizontal Spreading 391\u003c\/p\u003e \u003cp\u003e18.4.2.3 Vertical Spreading (Bottom-Up) 392\u003c\/p\u003e \u003cp\u003e18.4.2.4 Policies Refinement 395\u003c\/p\u003e \u003cp\u003e18.4.3 Structural Complexity Analysis of CP-ABE Policies 395\u003c\/p\u003e \u003cp\u003e18.4.3.1 Assessing the WSC for ABE Policies 396\u003c\/p\u003e \u003cp\u003e18.4.3.2 Assessing the Policies Generated in the Spreading Process 397\u003c\/p\u003e \u003cp\u003e18.4.4 Effectiveness Analysis 398\u003c\/p\u003e \u003cp\u003e18.4.4.1 Evaluation Metrics 399\u003c\/p\u003e \u003cp\u003e18.4.4.2 Adjusting the Interaction Graph to Assess Policy Effectiveness 400\u003c\/p\u003e \u003cp\u003e18.4.4.3 Method to Complement the User Interactions (Synthetic Edges Generation) 400\u003c\/p\u003e \u003cp\u003e18.4.5 Measuring Policy Effectiveness in the User Interaction Graph 403\u003c\/p\u003e \u003cp\u003e18.4.5.1 Simple Node-Based Strategy 403\u003c\/p\u003e \u003cp\u003e18.4.5.2 Weighted Node-Based Strategy 404\u003c\/p\u003e \u003cp\u003e18.5 Evaluation 405\u003c\/p\u003e \u003cp\u003e18.5.1 Dataset Description 405\u003c\/p\u003e \u003cp\u003e18.5.2 Results of the Complexity Evaluation 406\u003c\/p\u003e \u003cp\u003e18.5.3 Effectiveness Results From the Real Edges 407\u003c\/p\u003e \u003cp\u003e18.5.4 Effectiveness Results Using Real and Synthetic Edges 408\u003c\/p\u003e \u003cp\u003e18.5.4.1 Results of the Effectiveness Metrics for the Enhanced G + Graph 410\u003c\/p\u003e \u003cp\u003e18.6 Conclusions 413\u003c\/p\u003e \u003cp\u003eReferences 414\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 AI, ML, \u0026amp; Robotics in iSchools: An Academic Analysis for an Intelligent Societal Systems 417\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP. K. Paul\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 417\u003c\/p\u003e \u003cp\u003e19.2 Objective 419\u003c\/p\u003e \u003cp\u003e19.3 Methodology 420\u003c\/p\u003e \u003cp\u003e19.3.1 iSchools, Technologies, and Artificial Intelligence, ML, and Robotics 420\u003c\/p\u003e \u003cp\u003e19.4 Artificial Intelligence, ML, and Robotics: An Overview 427\u003c\/p\u003e \u003cp\u003e19.5 Artificial Intelligence, ML, and Robotics as an Academic Program: A Case on iSchools—North American Region 428\u003c\/p\u003e \u003cp\u003e19.6 Suggestions 431\u003c\/p\u003e \u003cp\u003e19.7 Motivation and Future Works 435\u003c\/p\u003e \u003cp\u003e19.8 Conclusion 435\u003c\/p\u003e \u003cp\u003eReferences 436\u003c\/p\u003e \u003cp\u003eIndex 439\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407144001879,"sku":"9781119762256","price":169.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119762256.jpg?v=1730498322","url":"https:\/\/bookcurl.com\/products\/machine-learning-techniques-and-analytics-for-cloud-security-9781119762256","provider":"Book Curl","version":"1.0","type":"link"}