Machine Learning Techniques and Analytics for Cloud Security

1. Auflage 2021
ISBN: 978-1-119-76225-6
Verlag: Wiley

Buch, Englisch, 480 Seiten, Format (B × H): 185 mm x 269 mm, Gewicht: 975 g

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.

Autoren/Hrsg.

Chakraborty, Rajdeep

Ghosh, Anupam

Mandal, Jyotsna Kumar

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Contents

Preface

Part I: Conceptual Aspects on Cloud and Applications of Machine Learning 1

1 Hybrid Cloud: A New Paradigm in Cloud Computing 3
Moumita Deb and Abantika Choudhury

1.1 Introduction 3

1.2 Hybrid Cloud 5

1.2.1 Architecture 6

1.2.2 Why Hybrid Cloud is Required? 6

1.2.3 Business and Hybrid Cloud 7

1.2.4 Things to Remember When Deploying Hybrid Cloud 8

1.3 Comparison Among Different Hybrid Cloud Providers 9

1.3.1 Cloud Storage and Backup Benefits 11

1.3.2 Pros and Cons of Different Service Providers 11

1.3.2.1 AWS Outpost 12

1.3.2.2 Microsoft Azure Stack 12

1.3.2.3 Google Cloud Anthos 12

1.3.3 Review on Storage of the Providers 13

1.3.3.1 AWS Outpost Storage 13

1.3.3.2 Google Cloud Anthos Storage 13

1.3.4 Pricing 15

1.4 Hybrid Cloud in Education 15

1.5 Significance of Hybrid Cloud Post-Pandemic 15

1.6 Security in Hybrid Cloud 16

1.6.1 Role of Human Error in Cloud Security 18

1.6.2 Handling Security Challenges 18

1.7 Use of AI in Hybrid Cloud 19

1.8 Future Research Direction 21

1.9 Conclusion 22

References 22

xix

v

2 Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework 25
Shillpi Mishrra

2.1 Introduction 25

2.2 Proposed Methodology 27

2.3 Result 28

2.3.1 Description of Datasets 29

2.3.2 Analysis of Result 29

2.3.3 Validation of Results 31

2.3.3.1 T-Test (Statistical Validation) 31

2.3.3.2 Statistical Validation 33

2.3.4 Glycan Cloud 37

2.4 Conclusions and Future Work 38

References 39

3 Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) 41
Subir Hazra, Alia Nikhat Khurshid and Akriti

3.1 Introduction 41

3.2 Related Methods 44

3.3 Methodology 46

3.3.1 Description 47

3.3.2 Flowchart 49

3.3.3 Algorithm 49

3.3.4 Interpretation of the Algorithm 50

3.3.5 Illustration 50

3.4 Result 51

3.4.1 Description of the Dataset 51

3.4.2 Result Analysis 51

3.4.3 Result Set Validation 52

3.5 Application in Cloud Domain 56

3.6 Conclusion 58

References 59

Part II: Cloud Security Systems Using Machine Learning Techniques 61

4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology 63
Soumen Santra, Partha Mukherjee and Arpan Deyasi

4.1 Introduction 64

4.2 Home Automation System 65

4.2.1 Sensors 65

4.2.2 Protocols 66

4.2.3 Technologies 66

4.2.4 Advantages 67

4.2.5 Disadvantages 67

4.3 Literature Review 67

4.4 Role of Sensors and Microcontrollers in Smart Home Design 68

4.5 Motivation of the Project 70

4.6 Smart Informative and Command Accepting Interface 70

4.7 Data Flow Diagram 71

4.8 Components of Informative Interface 72

4.9 Results 73

4.9.1 Circuit Design 73

4.9.2 LDR Data 76

4.9.3 API Data 76

4.10 Conclusion 78

4.11 Future Scope 78

References 78

5 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security 81
Anirban Bhowmik, Sunil Karforma and Joydeep Dey

5.1 Introduction 81

5.2 Literature Review 85

5.3 The Problem 86

5.4 Objectives and Contributions 86

5.5 Methodology 87

5.6 Results and Discussions 91

5.6.1 Statistical Analysis 93

5.6.2 Randomness Test of Key 94

5.6.3 Key Sensitivity Analysis 95

5.6.4 Security Analysis 96

5.6.5 Dataset Used on ANN 96

5.6.6 Comparisons 98

5.7 Conclusions 99

References 99

6 An Efficient Intrusion Detection System on Various Datasets Using Machine Learning Techniques 103
Debraj Chatterjee

6.1 Introduction 103

6.2 Motivation and Justification of the Proposed Work 104

6.3 Terminology Related to IDS 105

6.3.1 Network 105

6.3.2 Network Traffic 105

6.3.3 Intrusion 106

6.3.4 Intrusion Detection System 106

6.3.4.1 Various Types of IDS 108

6.3.4.2 Working Methodology of IDS 108

6.3.4.3 Characteristics of IDS 109

6.3.4.4 Advantages of IDS 110

6.3.4.5 Disadvantages of IDS 111

6.3.5 Intrusion Prevention System (IPS) 111

6.3.5.1 Network-Based Intrusion Prevention System (NIPS) 111

6.3.5.2 Wireless Intrusion Prevention System (WIPS) 112

6.3.5.3 Network Behavior Analysis (NBA) 112

6.3.5.4 Host-Based Intrusion Prevention System (HIPS) 112

6.3.6 Comparison of IPS With IDS/Relation Between IDS and IPS 112

6.3.7 Different Methods of Evasion in Networks 113

6.4 Intrusion Attacks on Cloud Environment 114

6.5 Comparative Studies 116

6.6 Proposed Methodology 121

6.7 Result 122

6.8 Conclusion and Future Scope 125

References 126

7 You Are Known by Your Mood: A Text-Based Sentiment Analysis for Cloud Security 129
Abhijit Roy and Parthajit Roy

7.1 Introduction 129

7.2 Literature Review 131

7.3 Essential Prerequisites 133

7.3.1 Security Aspects 133

7.3.2 Machine Learning Tools 135

7.3.2.1 Naïve Bayes Classifier 135

7.3.2.2 Artificial Neural Network 136

7.4 Proposed Model 136

7.5 Experimental Setup 138

7.6 Results and Discussions 139

7.7 Application in Cloud Security 142

7.7.1 Ask an Intelligent Security Question 142

7.7.2 Homomorphic Data Storage 142

7.7.3 Information Diffusion 144

7.8 Conclusion and Future Scope 144

References 145

8 The State-of-the-Art in Zero-Knowledge Authentication Proof for Cloud 149
Priyanka Ghosh

8.1 Introduction 149

8.2 Attacks and Countermeasures 153

8.2.1 Malware and Ransomware Breaches 154

8.2.2 Prevention of Distributing Denial of Service 154

8.2.3 Threat Detection 154

8.3 Zero-Knowledge Proof 154

8.4 Machine Learning for Cloud Computing 156

8.4.1 Types of Learning Algorithms 156

8.4.1.1 Supervised Learning 156

8.4.1.2 Supervised Learning Approach 156

8.4.1.3 Unsupervised Learning 157

8.4.2 Application on Machine Learning for Cloud Computing 157

8.4.2.1 Image Recognition 157

8.4.2.2 Speech Recognition 157

8.4.2.3 Medical Diagnosis 158

8.4.2.4 Learning Associations 158

8.4.2.5 Classification 158

8.4.2.6 Prediction 158

8.4.2.7 Extraction 158

8.4.2.8 Regression 158

8.4.2.9 Financial Services 159

8.5 Zero-Knowledge Proof: Details 159

8.5.1 Comparative Study 159

8.5.1.1 Fiat-Shamir ZKP Protocol 159

8.5.2 Diffie-Hellman Key Exchange Algorithm 161

8.5.2.1 Discrete Logarithm Attack 161

8.5.2.2 Man-in-the-Middle Attack 162

8.5.3 ZKP Version 1 162

8.5.4 ZKP Version 2 162

8.5.5 Analysis 164

8.5.6 Cloud Security Architecture 166

8.5.7 Existing Cloud Computing Architectures 167

8.5.8 Issues With Current Clouds 167

8.6 Conclusion 168

References 169

9 A Robust Approach for Effective Spam Detection Using Supervised Learning Techniques 171
Amartya Chakraborty, Suvendu Chattaraj, Sangita Karmakar and Shillpi Mishrra

9.1 Introduction 171

9.2 Literature Review 173

9.3 Motivation 174

9.4 System Overview 175

9.5 Data Description 176

9.6 Data Processing 176

9.7 Feature Extraction 178

9.8 Learning Techniques Used 179

9.8.1 Support Vector Machine 179

9.8.2 k-Nearest Neighbors 180

9.8.3 Decision Tree 180

9.8.4 Convolutional Neural Network 180

Über Autor(innen)

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.