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MACHINE 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 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.

Preface xix
Part I Conceptual Aspects on Cloud and Applications of Machine Learning
1(60)
1 Hybrid Cloud: A New Paradigm in Cloud Computing
3(22)
Moumita Deb
Abantika Choudhury
1.1 Introduction
3(2)
1.2 Hybrid Cloud
5(4)
1.2.1 Architecture
6(1)
1.2.2 Why Hybrid Cloud is Required?
6(1)
1.2.3 Business and Hybrid Cloud
7(1)
1.2.4 Things to Remember When Deploying Hybrid Cloud
8(1)
1.3 Comparison Among Different Hybrid Cloud Providers
9(6)
1.3.1 Cloud Storage and Backup Benefits
11(1)
1.3.2 Pros and Cons of Different Service Providers
11(1)
1.3.2.1 AWS Outpost
12(1)
1.3.2.2 Microsoft Azure Stack
12(1)
1.3.2.3 Google Cloud Anthos
12(1)
1.3.3 Review on Storage of the Providers
13(1)
1.3.3.1 AWS Outpost Storage
13(1)
1.3.3.2 Google Cloud Anthos Storage
13(2)
1.3.4 Pricing
15(1)
1.4 Hybrid Cloud in Education
15(1)
1.5 Significance of Hybrid Cloud Post-Pandemic
15(1)
1.6 Security in Hybrid Cloud
16(3)
1.6.1 Role of Human Error in Cloud Security
18(1)
1.6.2 Handling Security Challenges
18(1)
1.7 Use of AI in Hybrid Cloud
19(2)
1.8 Future Research Direction
21(1)
1.9 Conclusion
22(3)
References
22(3)
2 Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework
25(16)
Shillpi Mishrra
2.1 Introduction
25(2)
2.2 Proposed Methodology
27(1)
2.3 Result
28(10)
2.3.1 Description of Datasets
29(1)
2.3.2 Analysis of Result
29(2)
2.3.3 Validation of Results
31(1)
2.3.3.1 T-Test (Statistical Validation)
31(2)
2.3.3.2 Statistical Validation
33(4)
2.3.4 Glycan Cloud
37(1)
2.4 Conclusions and Future Work
38(3)
References
39(2)
3 Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR)
41(20)
Subir Hazra
Alia Nikhat Khurshid
Akriti
3.1 Introduction
41(3)
3.2 Related Methods
44(2)
3.3 Methodology
46(5)
3.3.1 Description
47(2)
3.3.2 Flowchart
49(1)
3.3.3 Algorithm
49(1)
3.3.4 Interpretation of the Algorithm
50(1)
3.3.5 Illustration
50(1)
3.4 Result
51(5)
3.4.1 Description of the Dataset
51(1)
3.4.2 Result Analysis
51(1)
3.4.3 Result Set Validation
52(4)
3.5 Application in Cloud Domain
56(2)
3.6 Conclusion
58(3)
References
59(2)
Part II Cloud Security Systems Using Machine Learning Techniques
61(152)
4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology
63(18)
Soumen Santra
Partha Mukherjee
Arpan Deyasi
4.1 Introduction
64(1)
4.2 Home Automation System
65(2)
4.2.1 Sensors
65(1)
4.2.2 Protocols
66(1)
4.2.3 Technologies
66(1)
4.2.4 Advantages
67(1)
4.2.5 Disadvantages
67(1)
4.3 Literature Review
67(1)
4.4 Role of Sensors and Microcontrollers in Smart Home Design
68(2)
4.5 Motivation of the Project
70(1)
4.6 Smart Informative and Command Accepting Interface
70(1)
4.7 Data Flow Diagram
71(1)
4.8 Components of Informative Interface
72(1)
4.9 Results
73(5)
4.9.1 Circuit Design
73(3)
4.9.2 LDR Data
76(1)
4.9.3 API Data
76(2)
4.10 Conclusion
78(1)
4.11 Future Scope
78(3)
References
78(3)
5 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security
81(22)
Anirban Bhowtnik
Sunil Karforma
Joydeep Dey
5.1 Introduction
81(4)
5.2 Literature Review
85(1)
5.3 The Problem
86(1)
5.4 Objectives and Contributions
86(1)
5.5 Methodology
87(4)
5.6 Results and Discussions
91(8)
5.6.1 Statistical Analysis
93(1)
5.6.2 Randomness Test of Key
94(1)
5.6.3 Key Sensitivity Analysis
95(1)
5.6.4 Security Analysis
96(1)
5.6.5 Dataset Used on ANN
96(2)
5.6.6 Comparisons
98(1)
5.7 Conclusions
99(4)
References
99(4)
6 An Efficient Intrusion Detection System on Various Datasets Using Machine Learning Techniques
103(26)
Debraj Chatteljee
6.1 Introduction
103(1)
6.2 Motivation and Justification of the Proposed Work
104(1)
6.3 Terminology Related to IDS
105(9)
6.3.1 Network
105(1)
6.3.2 Network Traffic
105(1)
6.3.3 Intrusion
106(1)
6.3.4 Intrusion Detection System
106(2)
6.3.4.1 Various Types of IDS
108(1)
6.3.4.2 Working Methodology of IDS
108(1)
6.3.4.3 Characteristics of IDS
109(1)
6.3.4.4 Advantages of IDS
110(1)
6.3.4.5 Disadvantages of IDS
111(1)
6.3.5 Intrusion Prevention System (IPS)
111(1)
6.3.5.1 Network-Based Intrusion Prevention System (NIPS)
111(1)
6.3.5.2 Wireless Intrusion Prevention System (WIPS)
112(1)
6.3.5.3 Network Behavior Analysis (NBA)
112(1)
6.3.5.4 Host-Based Intrusion Prevention System (HIPS)
112(1)
6.3.6 Comparison of IPS With IDS/Relation Between IDS and IPS
112(1)
6.3.7 Different Methods of Evasion in Networks
113(1)
6.4 Intrusion Attacks on Cloud Environment
114(2)
6.5 Comparative Studies
116(5)
6.6 Proposed Methodology
121(1)
6.7 Result
122(3)
6.8 Conclusion and Future Scope
125(4)
References
126(3)
7 You Are Known by Your Mood: A Text-Based Sentiment Analysis for Cloud Security
129(20)
Abhijit Roy
Parthajit Roy
7.1 Introduction
129(2)
7.2 Literature Review
131(2)
7.3 Essential Prerequisites
133(3)
7.3.1 Security Aspects
133(2)
7.3.2 Machine Learning Tools
135(1)
7.3.2.1 Naive Bayes Classifier
135(1)
7.3.2.2 Artificial Neural Network
136(1)
7.4 Proposed Model
136(2)
7.5 Experimental Setup
138(1)
7.6 Results and Discussions
139(3)
7.7 Application in Cloud Security
142(2)
7.7.1 Ask an Intelligent Security Question
142(1)
7.7.2 Homomorphic Data Storage
142(2)
7.7.3 Information Diffusion
144(1)
7.8 Conclusion and Future Scope
144(5)
References
145(4)
8 The State-of-the-Art in Zero-Knowledge Authentication Proof for Cloud
149(22)
Priyanka Ghosh
8.1 Introduction
149(4)
8.2 Attacks and Countermeasures
153(1)
8.2.1 Malware and Ransomware Breaches
154(1)
8.2.2 Prevention of Distributing Denial of Service
154(1)
8.2.3 Threat Detection
154(1)
8.3 Zero-Knowledge Proof
154(2)
8.4 Machine Learning for Cloud Computing
156(3)
8.4.1 Types of Learning Algorithms
156(1)
8.4.1.1 Supervised Learning
156(1)
8.4.1.2 Supervised Learning Approach
156(1)
8.4.1.3 Unsupervised Learning
157(1)
8.4.2 Application on Machine Learning for Cloud Computing
157(1)
8.4.2.1 Image Recognition
157(1)
8.4.2.2 Speech Recognition
157(1)
8.4.2.3 Medical Diagnosis
158(1)
8.4.2.4 Learning Associations
158(1)
8.4.2.5 Classification
158(1)
8.4.2.6 Prediction
158(1)
8.4.2.7 Extraction
158(1)
8.4.2.8 Regression
158(1)
8.4.2.9 Financial Services
159(1)
8.5 Zero-Knowledge Proof: Details
159(9)
8.5.1 Comparative Study
159(1)
8.5.1.1 Fiat-Shamir ZKP Protocol
159(2)
8.5.2 Diffie-Hellman Key Exchange Algorithm
161(1)
8.5.2.1 Discrete Logarithm Attack
161(1)
8.5.2.2 Man-in-the-Middle Attack
162(1)
8.5.3 ZKP Version 1
162(1)
8.5.4 ZKP Version 2
162(2)
8.5.5 Analysis
164(2)
8.5.6 Cloud Security Architecture
166(1)
8.5.7 Existing Cloud Computing Architectures
167(1)
8.5.8 Issues With Current Clouds
167(1)
8.6 Conclusion
168(3)
References
169(2)
9 A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques
171(22)
Amartya Chakraborty
Suvendu Chattaraj
Sangita Karmakar
Shillpi Mishrra
9.1 Introduction
171(2)
9.2 Literature Review
173(1)
9.3 Motivation
174(1)
9.4 System Overview
175(1)
9.5 Data Description
176(1)
9.6 Data Processing
176(2)
9.7 Feature Extraction
178(1)
9.8 Learning Techniques Used
179(3)
9.8.1 Support Vector Machine
179(1)
9.8.2 k-Nearest Neighbors
180(1)
9.8.3 Decision Tree
180(1)
9.8.4 Convolutional Neural Network
180(2)
9.9 Experimental Setup
182(1)
9.10 Evaluation Metrics
183(2)
9.11 Experimental Results
185(3)
9.11.1 Observations in Comparison With State-of-the-Art
187(1)
9.12 Application in Cloud Architecture
188(1)
9.13 Conclusion
189(4)
References
190(3)
10 An Intelligent System for Securing Network From Intrusion Detection and Prevention of Phishing Attack Using Machine Learning Approaches
193(20)
Sumit Banik
Sagar Banik
Anupam Mukherjee
10.1 Introduction
193(4)
10.1.1 Types of Phishing
195(1)
10.1.1.1 Spear Phishing
195(1)
10.1.1.2 Whaling
195(1)
10.1.1.3 Catphishing and Catfishing
195(1)
10.1.1.4 Clone Phishing
196(1)
10.1.1.5 Voice Phishing
196(1)
10.1.2 Techniques of Phishing
196(1)
10.1.2.1 Link Manipulation
196(1)
10.1.2.2 Filter Evasion
196(1)
10.1.2.3 Website Forgery
196(1)
10.1.2.4 Covert Redirect
197(1)
10.2 Literature Review
197(2)
10.3 Materials and Methods
199(5)
10.3.1 Dataset and Attributes
199(1)
10.3.2 Proposed Methodology
199(3)
10.3.2.1 Logistic Regression
202(1)
10.3.2.2 Naive Bayes
202(1)
10.3.2.3 Support Vector Machine
203(1)
10.3.2.4 Voting Classification
203(1)
10.4 Result Analysis
204(6)
10.4.1 Analysis of Different Parameters for ML Models
204(1)
10.4.2 Predictive Outcome Analysis in Phishing URLs Dataset
205(1)
10.4.3 Analysis of Performance Metrics
206(4)
10.4.4 Statistical Analysis of Results
210(1)
10.4.4.1 ANOVA: Two-Factor Without Replication
210(1)
10.4.4.2 ANOVA: Single Factor
210(1)
10.5 Conclusion
210(3)
References
211(2)
Part III Cloud Security Analysis Using Machine Learning Techniques
213(100)
11 Cloud Security Using Honeypot Network and Blockchain: A Review
215(24)
Smarta Sangui
Swarup Kr Ghosh
11.1 Introduction
215(1)
11.2 Cloud Computing Overview
216(5)
11.2.1 Types of Cloud Computing Services
216(1)
11.2.1.1 Software as a Service
216(2)
11.2.1.2 Infrastructure as a Service
218(1)
11.2.1.3 Platform as a Service
218(1)
11.2.2 Deployment Models of Cloud Computing
218(1)
11.2.2.1 Public Cloud
218(1)
11.2.2.2 Private Cloud
218(1)
11.2.2.3 Community Cloud
219(1)
11.2.2.4 Hybrid Cloud
219(1)
11.2.3 Security Concerns in Cloud Computing
219(1)
11.2.3.1 Data Breaches
219(1)
11.2.3.2 Insufficient Change Control and Misconfiguration
219(1)
11.2.3.3 Lack of Strategy and Security Architecture
220(1)
11.2.3.4 Insufficient Identity, Credential, Access, and Key Management
220(1)
11.2.3.5 Account Hijacking
220(1)
11.2.3.6 Insider Threat
220(1)
11.2.3.7 Insecure Interfaces and APIs
220(1)
11.2.3.8 Weak Control Plane
221(1)
11.3 Honeypot System
221(6)
11.3.1 VM (Virtual Machine) as Honeypot in the Cloud
221(1)
11.3.2 Attack Sensing and Analyzing Framework
222(1)
11.3.3 A Fuzzy Technique Against Fingerprinting Attacks
223(1)
11.3.4 Detecting and Classifying Malicious Access
224(1)
11.3.5 A Bayesian Defense Model for Deceptive Attack
224(2)
11.3.6 Strategic Game Model for DDoS Attacks in Smart Grid
226(1)
11.4 Blockchain
227(6)
11.4.1 Blockchain-Based Encrypted Cloud Storage
228(1)
11.4.2 Cloud-Assisted EHR Sharing via Consortium Blockchain
229(1)
11.4.3 Blockchain-Secured Cloud Storage
230(1)
11.4.4 Blockchain and Edge Computing-Based Security Architecture
230(1)
11.4.5 Data Provenance Architecture in Cloud Ecosystem Using Blockchain
231(2)
11.6 Comparative Analysis
233(1)
11.7 Conclusion
233(6)
References
234(5)
12 Machine Learning-Based Security in Cloud Database--A Survey
239(32)
Utsav Vora
Jayleena Mahato
Hrishav Dasgupta
Anand Kumar
Swarup Kr Ghosh
12.1 Introduction
239(2)
12.2 Security Threats and Attacks
241(3)
12.3 Dataset Description
244(1)
12.3.1 NSL-KDD Dataset
244(1)
12.3.2 UNSW-NB15 Dataset
244(1)
12.4 Machine Learning for Cloud Security
245(17)
12.4.1 Supervised Learning Techniques
245(1)
12.4.1.1 Support Vector Machine
245(2)
12.4.1.2 Artificial Neural Network
247(2)
12.4.1.3 Deep Learning
249(1)
12.4.1.4 Random Forest
250(1)
12.4.2 Unsupervised Learning Techniques
251(1)
12.4.2.1 K-Means Clustering
252(1)
12.4.2.2 Fuzzy C-Means Clustering
253(1)
12.4.2.3 Expectation-Maximization Clustering
253(1)
12.4.2.4 Cuckoo Search With Particle Swarm Optimization (PSO)
254(2)
12.4.3 Hybrid Learning Techniques
256(1)
12.4.3.1 HIDCC: Hybrid Intrusion Detection Approach in Cloud Computing
256(1)
12.4.3.2 Clustering-Based Hybrid Model in Deep Learning Framework
257(1)
12.4.3.3 K-Nearest Neighbor-Based Fuzzy C-Means Mechanism
258(2)
12.4.3.4 K-Means Clustering Using Support Vector Machine
260(1)
12.4.3.5 K-Nearest Neighbor-Based Artificial Neural Network Mechanism
260(1)
12.4.3.6 Artificial Neural Network Fused With Support Vector Machine
261(1)
12.4.3.7 Particle Swarm Optimization-Based Probabilistic Neural Network
261(1)
12.5 Comparative Analysis
262(2)
12.6 Conclusion
264(7)
References
267(4)
13 Machine Learning Adversarial Attacks: A Survey Beyond
271(22)
Chandni Magoo
Puneet Garg
13.1 Introduction
271(1)
13.2 Adversarial Learning
272(1)
13.2.1 Concept
272(1)
13.3 Taxonomy of Adversarial Attacks
273(3)
13.3.1 Attacks Based on Knowledge
273(1)
13.3.1.1 Black Box Attack (Transferable Attack)
273(1)
13.3.1.2 White Box Attack
274(1)
13.3.2 Attacks Based on Goals
275(1)
13.3.2.1 Target Attacks
275(1)
13.3.2.2 Non-Target Attacks
275(1)
13.3.3 Attacks Based on Strategies
275(1)
13.3.3.1 Poisoning Attacks
275(1)
13.3.3.2 Evasion Attacks
276(1)
13.3.4 Textual-Based Attacks (NLP)
276(1)
13.3.4.1 Character Level Attacks
276(1)
13.3.4.2 Word-Level Attacks
276(1)
13.3.4.3 Sentence-Level Attacks
276(1)
13.4 Review of Adversarial Attack Methods
276(11)
13.4.1 L-BFGS
277(1)
13.4.2 Feedforward Derivation Attack (Jacobian Attack)
277(1)
13.4.3 Fast Gradient Sign Method
278(1)
13.4.4 Methods of Different Text-Based Adversarial Attacks
278(6)
13.4.5 Adversarial Attacks Methods Based on Language Models
284(1)
13.4.6 Adversarial Attacks on Recommender Systems
284(1)
13.4.6.1 Random Attack
284(2)
13.4.6.2 Average Attack
286(1)
13.4.6.3 Bandwagon Attack
286(1)
13.4.6.4 Reverse Bandwagon Attack
286(1)
13.5 Adversarial Attacks on Cloud-Based Platforms
287(1)
13.6 Conclusion
288(5)
References
288(5)
14 Protocols for Cloud Security
293(20)
Weijing You
Bo Chen
14.1 Introduction
293(2)
14.2 System and Adversarial Model
295(1)
14.2.1 System Model
295(1)
14.2.2 Adversarial Model
295(1)
14.3 Protocols for Data Protection in Secure Cloud Computing
296(5)
14.3.1 Homomorphic Encryption
297(1)
14.3.2 Searchable Encryption
298(1)
14.3.3 Attribute-Based Encryption
299(1)
14.3.4 Secure Multi-Party Computation
300(1)
14.4 Protocols for Data Protection in Secure Cloud Storage
301(8)
14.4.1 Proofs of Encryption
301(2)
14.4.2 Secure Message-Locked Encryption
303(1)
14.4.3 Proofs of Storage
303(2)
14.4.4 Proofs of Ownership
305(1)
14.4.5 Proofs of Reliability
306(3)
14.5 Protocols for Secure Cloud Systems
309(1)
14.6 Protocols for Cloud Security in the Future
309(1)
14.7 Conclusion
310(3)
References
311(2)
Part IV Case Studies Focused on Cloud Security
313(66)
15 A Study on Google Cloud Platform (GCP) and Its Security
315(24)
Agniswar Roy
Abhik Banerjee
Navneet Bhardwaj
15.1 Introduction
315(3)
15.1.1 Google Cloud Platform Current Market Holding
316(1)
15.1.1.1 The Forrester Wave
317(1)
15.1.1.2 Gartner Magic Quadrant
317(1)
15.1.2 Google Cloud Platform Work Distribution
317(1)
15.1.2.1 SaaS
318(1)
15.1.2.2 PaaS
318(1)
15.1.2.3 IaaS
318(1)
15.1.2.4 On-Premise
318(1)
15.2 Google Cloud Platform's Security Features Basic Overview
318(3)
15.2.1 Physical Premises Security
319(1)
15.2.2 Hardware Security
319(1)
15.2.3 Inter-Service Security
319(1)
15.2.4 Data Security
320(1)
15.2.5 Internet Security
320(1)
15.2.6 In-Software Security
320(1)
15.2.7 End User Access Security
321(1)
15.3 Google Cloud Platform's Architecture
321(3)
15.3.1 Geographic Zone
321(1)
15.3.2 Resource Management
322(1)
15.3.2.1 IAM
322(1)
15.3.2.2 Roles
323(1)
15.3.2.3 Billing
323(1)
15.4 Key Security Features
324(6)
15.4.1 IAP
324(1)
15.4.2 Compliance
325(1)
15.4.3 Policy Analyzer
326(1)
15.4.4 Security Command Center
326(1)
15.4.4.1 Standard Tier
326(1)
15.4.4.2 Premium Tier
326(3)
15.4.5 Data Loss Protection
329(1)
15.4.6 Key Management
329(1)
15.4.7 Secret Manager
330(1)
15.4.8 Monitoring
330(1)
15.5 Key Application Features
330(2)
15.5.1 Stackdriver (Currently Operations)
330(1)
15.5.1.1 Profiler
330(1)
15.5.1.2 Cloud Debugger
330(1)
15.5.1.3 Trace
331(1)
15.5.2 Network
331(1)
15.5.3 Virtual Machine Specifications
332(1)
15.5.4 Preemptible VMs
332(1)
15.6 Computation in Google Cloud Platform
332(1)
15.6.1 Compute Engine
332(1)
15.6.2 App Engine
333(1)
15.6.3 Container Engine
333(1)
15.6.4 Cloud Functions
333(1)
15.7 Storage in Google Cloud Platform
333(1)
15.8 Network in Google Cloud Platform
334(1)
15.9 Data in Google Cloud Platform
334(1)
15.10 Machine Learning in Google Cloud Platform
335(1)
15.11 Conclusion
335(4)
References
337(2)
16 Case Study of Azure and Azure Security Practices
339(18)
Navneet Bhardwaj
Abhik Barterjee
Agniswar Roy
16.1 Introduction
339(2)
16.1.1 Azure Current Market Holding
340(1)
16.1.2 The Forrester Wave
340(1)
16.1.3 Gartner Magic Quadrant
340(1)
16.2 Microsoft Azure--The Security Infrastructure
341(1)
16.2.1 Azure Security Features and Tools
341(1)
16.2.2 Network Security
342(1)
16.3 Data Encryption
342(2)
16.3.1 Data Encryption at Rest
342(1)
16.3.2 Data Encryption at Transit
342(1)
16.3.3 Asset and Inventory Management
343(1)
16.3.4 Azure Marketplace
343(1)
16.4 Azure Cloud Security Architecture
344(2)
16.4.1 Working
344(1)
16.4.2 Design Principles
344(1)
16.4.2.1 Alignment of Security Policies
344(1)
16.4.2.2 Building a Comprehensive Strategy
345(1)
16.4.2.3 Simplicity Driven
345(1)
16.4.2.4 Leveraging Native Controls
345(1)
16.4.2.5 Identification-Based Authentication
345(1)
16.4.2.6 Accountability
345(1)
16.4.2.7 Embracing Automation
345(1)
16.4.2.8 Stress on Information Protection
345(1)
16.4.2.9 Continuous Evaluation
346(1)
16.4.2.10 Skilled Workforce
346(1)
16.5 Azure Architecture
346(4)
16.5.1 Components
346(1)
16.5.1.1 Azure Api Gateway
346(1)
16.5.1.2 Azure Functions
346(1)
16.5.2 Services
347(1)
16.5.2.1 Azure Virtual Machine
347(1)
16.5.2.2 Blob Storage
347(1)
16.5.2.3 Azure Virtual Network
348(1)
16.5.2.4 Content Delivery Network
348(1)
16.5.2.5 Azure SQL Database
349(1)
16.6 Features of Azure
350(1)
16.6.1 Key Features
350(1)
16.6.1.1 Data Resiliency
350(1)
16.6.1.2 Data Security
350(1)
16.6.1.3 BCDR Integration
350(1)
16.6.1.4 Storage Management
351(1)
16.6.1.5 Single Pane View
351(1)
16.7 Common Azure Security Features
351(4)
16.7.1 Security Center
351(1)
16.7.2 Key Vault
351(1)
16.7.3 Azure Active Directory
352(1)
16.7.3.1 Application Management
352(1)
16.7.3.2 Conditional Access
352(1)
16.7.3.3 Device Identity Management
352(1)
16.7.3.4 Identity Protection
353(1)
16.7.3.5 Azure Sentinel
353(1)
16.7.3.6 Privileged Identity Management
354(1)
16.7.3.7 Multifactor Authentication
354(1)
16.7.3.8 Single Sign On
354(1)
16.8 Conclusion
355(2)
References
355(2)
17 Nutanix Hybrid Cloud From Security Perspective
357(22)
Abhik Banerjee
Agniswar Roy
Amar Kalvikatte
Navneet Bhardwaj
17.1 Introduction
357(1)
17.2 Growth of Nutanix
358(3)
17.2.1 Gartner Magic Quadrant
358(1)
17.2.2 The Forrester Wave
358(1)
17.2.3 Consumer Acquisition
359(1)
17.2.4 Revenue
359(2)
17.3 Introductory Concepts
361(1)
17.3.1 Plane Concepts
361(1)
17.3.1.1 Control Plane
361(1)
17.3.1.2 Data Plane
361(1)
17.3.2 Security Technical Implementation Guides
362(1)
17.3.3 SaltStack and SCMA
362(1)
17.4 Nutanix Hybrid Cloud
362(5)
17.4.1 Prism
362(1)
17.4.1.1 Prism Element
363(1)
17.4.1.2 Prism Central
364(1)
17.4.2 Acropolis
365(1)
17.4.2.1 Distributed Storage Fabric
365(2)
17.4.2.2 AHV
367(1)
17.5 Reinforcing AHV and Controller VM
367(1)
17.6 Disaster Management and Recovery
368(3)
17.6.1 Protection Domains and Consistent Groups
368(1)
17.6.2 Nutanix DSF Replication of OpLog
369(1)
17.6.3 DSF Snapshots and VmQueisced Snapshot Service
370(1)
17.6.4 Nutanix Cerebro
370(1)
17.7 Security and Policy Management on Nutanix Hybrid Cloud
371(3)
17.7.1 Authentication on Nutanix
372(1)
17.7.2 Nutanix Data Encryption
372(1)
17.7.3 Security Policy Management
373(1)
17.7.3.1 Enforcing a Policy
374(1)
17.7.3.2 Priority of a Policy
374(1)
17.7.3.3 Automated Enforcement
374(1)
17.8 Network Security and Log Management
374(2)
17.8.1 Segmented and Unsegmented Network
375(1)
17.9 Conclusion
376(3)
References
376(3)
Part V Policy Aspects
379(57)
18 A Data Science Approach Based on User Interactions to Generate Access Control Policies for Large Collections of Documents
381(36)
Jedidiah Yanez-Sierra
Arturo Diaz-Perez
Victor Sosa-Sosa
18.1 Introduction
381(2)
18.2 Related Work
383(1)
18.3 Network Science Theory
384(3)
18.4 Approach to Spread Policies Using Networks Science
387(18)
18.4.1 Finding the Most Relevant Spreaders
388(1)
18.4.1.1 Weighting Users
389(1)
18.4.1.2 Selecting the Top V Spreaders
390(1)
18.4.2 Assign and Spread the Access Control Policies
390(1)
18.4.2.1 Access Control Policies
391(1)
18.4.2.2 Horizontal Spreading
391(1)
18.4.2.3 Vertical Spreading (Bottom-Up)
392(3)
18.4.2.4 Policies Refinement
395(1)
18.4.3 Structural Complexity Analysis of CP-ABE Policies
395(1)
18.4.3.1 Assessing the WSC for ABE Policies
396(1)
18.4.3.2 Assessing the Policies Generated in the Spreading Process
397(1)
18.4.4 Effectiveness Analysis
398(1)
18.4.4.1 Evaluation Metrics
399(1)
18.4.4.2 Adjusting the Interaction Graph to Assess Policy Effectiveness
400(1)
18.4.4.3 Method to Complement the User Interactions (Synthetic Edges Generation)
400(3)
18.4.5 Measuring Policy Effectiveness in the User Interaction Graph
403(1)
18.4.5.1 Simple Node-Based Strategy
403(1)
18.4.5.2 Weighted Node-Based Strategy
404(1)
18.5 Evaluation
405(8)
18.5.1 Dataset Description
405(1)
18.5.2 Results of the Complexity Evaluation
406(1)
18.5.3 Effectiveness Results From the Real Edges
407(1)
18.5.4 Effectiveness Results Using Real and Synthetic Edges
408(2)
18.5.4.1 Results of the Effectiveness Metrics for the Enhanced G+ Graph
410(3)
18.6 Conclusions
413(4)
References
414(3)
19 AI, ML, & Robotics in iSchools: An Academic Analysis for an Intelligent Societal Systems
417(19)
P. K. Paul
19.1 Introduction
417(2)
19.2 Objective
419(1)
19.3 Methodology
420(7)
19.3.1 iSchools, Technologies, and Artificial Intelligence, ML, and Robotics
420(7)
19.4 Artificial Intelligence, ML, and Robotics: An Overview
427(1)
19.5 Artificial Intelligence, ML, and Robotics as an Academic Program: A Case on iSchools--North American Region
428(3)
19.6 Suggestions
431(4)
19.7 Motivation and Future Works
435(1)
19.8 Conclusion
435(1)
References 436(3)
Index 439
Rajdeep Chakraborty obtained his PhD in CSE from the University of Kalyani. He is currently an assistant professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia, Kolkata, India. He has several publications in reputed international journals and conferences and has authored a book on hardware cryptography. His field of interest is mainly in cryptography and computer security.

Anupam Ghosh obtained his PhD in Engineering from Jadavpur University. He is currently a professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata. He has published more than 80 papers in reputed international journals and conferences. His field of interest is mainly in AI, machine learning, deep learning, image processing, soft computing, bioinformatics, IoT, data mining.

Jyotsna Kumar Mandal obtained his PhD in CSE from Jadavpur University He has more than 450 publications in reputed international journals and conferences. His field of interest is mainly in coding theory, data and network security, remote sensing & GIS-based applications, data compression error corrections, information security, watermarking, steganography and document authentication, image processing, visual cryptography, MANET, wireless and mobile computing/security, unify computing, chaos theory, and applications.
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