Preface xv
Acknowledgments xxiii
1 Securing Cloud-Based Enterprise Applications and Its Data 1
Subhradip Debnath, Aniket Das and Budhaditya Sarkar
1.1 Introduction 2
1.2 Background and Related Works 3
1.3 System Design and Architecture 5
1.3.1 Proposed System Design and Architecture 5
1.3.2 Modules 5
1.3.2.1 Compute Instances 5
1.3.2.2 API Gateway 6
1.3.2.3 Storage Bucket (Amazon S3) 6
1.3.2.4 Lambda 6
1.3.2.5 Load Balancer 6
1.3.2.6 Internet Gateway 6
1.3.2.7 Security Groups 7
1.3.2.8 Autoscaling 7
1.3.2.9 QLDB 7
1.3.2.10 NoSQL Database 8
1.3.2.11 Linux Instance and Networking 8
1.3.2.12 Virtual Network and Subnet Configuration 8
1.4 Methodology 9
1.4.1 Firewall 9
1.4.2 Malware Injection Prevention 9
1.4.3 Man-in-the-Middle Prevention 9
1.4.4 Data at Transit and SSL 9
1.4.5 Data Encryption at Rest 10
1.4.6 Centralized Ledger Database 10
1.4.7 NoSQL Database 10
1.4.8 Linux Instance and Server Side Installations 10
1.5 Performance Analysis 21
1.5.1 Load Balancer 21
1.5.2 Lambda (For Compression of Data) 22
1.5.3 Availability Zone 23
1.5.4 Data in Transit (Encryption) 23
1.5.5 Data in Rest (Encryption) 23
1.6 Future Research Direction 23
1.7 Conclusion 24
References 25
2 High-Performance Computing-Based Scalable "Cloud Forensicsas- a-Service" Readiness Framework Factors-A Review 27
Srinivasa Rao Gundu, Charanarur Panem and S. Satheesh
2.1 Introduction 28
2.2 Aim of the Study 29
2.3 Motivation for the Study 29
2.4 Literature Review 30
2.5 Research Methodology 32
2.6 Testing Environment Plan 32
2.7 Testing 35
2.7.1 Scenario 1: Simultaneous Imaging and Upload and Encryption 36
2.7.2 Scenario 2: Real-Time Stream Processing 41
2.7.3 Scenario 3: Remote Desktop Connection, Performance Test 41
2.8 Recommendations 42
2.9 Limitations of Present Study 42
2.10 Conclusions 43
2.11 Scope for the Future Work 43
Acknowledgements 44
References 44
3 Malware Identification, Analysis and Similarity 47
Subhradip Debnath and Soumyanil Biswas
3.1 Introduction 48
3.1.1 Goals of Malware Analysis and Malware Identification 48
3.1.2 Common Malware Analysis Techniques 49
3.2 Background and Related Works 49
3.3 Proposed System Design Architecture 51
3.3.1 Tool Requirement, System Design, and Architecture 51
3.3.1.1 For Static Malware Analysis 51
3.3.1.2 For Dynamic Malware Analysis 56
3.4 Methodology 62
3.5 Performance Analysis 67
3.6 Future Research Direction 67
3.7 Conclusion 68
References 68
4 Robust Fraud Detection Mechanism 71
Balajee Maram, Veerraju Gampala, Satish Muppidi and T. Daniya
4.1 Introduction 72
4.2 Related Work 76
4.2.1 Blockchain Technology for Online Business 76
4.2.2 Validation and Authentication 79
4.2.3 Types of Online Shopping Fraud 81
4.2.3.1 Software Fraudulent of Online Shopping 81
4.2.4 Segmentation/Authentication 82
4.2.4.1 Secure Transaction Though Segmentation Algorithm 83
4.2.4.2 Critical Path Segmentation Optimization 85
4.2.5 Role of Blockchain Technology for Supply Chain and Logistics 87
4.3 Conclusion 91
References 92
5 Blockchain-Based Identity Management Systems 95
Ramani Selvanambi, Bhavya Taneja, Priyal Agrawal, Henil Jayesh Thakor and Marimuthu Karuppiah
5.1 Introduction 96
5.2 Preliminaries 99
5.2.1 Identity Management Systems 99
5.2.1.1 Identity Factors 99
5.2.1.2 Architecture of Identity Management Systems 99
5.2.1.3 Types of Identity Management Systems 100
5.2.1.4 Importance of Identity Management Systems 101
5.2.2 Blockchain 102
5.2.2.1 Blockchain Architecture 102
5.2.2.2 Components of Blockchain Architecture 102
5.2.2.3 Merkle Tree 103
5.2.2.4 Consensus Algorithm 103
5.2.2.5 Types of Blockchain Architecture 105
5.2.3 Challenges 106
5.3 Blockchain-Based Identity Management System 109
5.3.1 Need for Blockchain-Based Identity Management Systems 109
5.3.2 Approaches for Blockchain-Based Identity Management Systems 110
5.3.3 Blockchain-Based Identity Management System Implementations 111
5.3.4 Impact of Using Blockchain-Based Identity Management on Business and Users 120
5.3.5 Various Use Cases of Blockchain Identity Management 121
5.4 Discussion 122
5.4.1 Challenges Related to Identity 122
5.4.2 Cost Implications 123
5.5 Conclusion 123
5.6 Future Scope 124
References 125
6 Insights Into Deep Steganography: A Study of Steganography Automation and Trends 129
R. Gurunath, Debabrata Samanta and Digvijay Pandey
6.1 Introduction 130
6.2 Convolution Network Learning 131
6.2.1 CNN Issues 132
6.3 Recurrent Neural Networks 133
6.3.1 RNN Forward Propagation 135
6.4 Long Short-Term Memory Networks 136
6.4.1 LSTM Issues 137
6.5 Back Propagation in Neural Networks 138
6.6 Literature Survey on Neural Networks in Steganography 140
6.6.1 TS-RNN: Text Steganalysis Based on Recurrent Neural Networks 140
6.6.2 Generative Text Steganography Based on LSTM Network and Attention Mechanism with Keywords 141
6.6.3 Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph 142
6.6.4 RITS: Real-Time Interactive Text Steganography Based on Automatic Dialogue Model 143
6.6.5 Steganalysis and Payload Estimation of Embedding in Pixel Differences Using Neural Networks 144
6.6.6 Reversible Data Hiding Using Multilayer Perceptron-Based Pixel Prediction 144
6.6.7 Neural Network-Based Steganography Algorithm for Still Images 145
6.7 Optimization Algorithms in Neural Networks 145
6.7.1 Gradient Descent 145
6.7.1.1 GD Issues 146
6.7.2 Stochastic Gradient Descent 147
6.7.2.1 SGD Issues 148
6.7.3 SGD with Momentum 148
6.7.4 Mini Batch SGD 149
6.7.4.1 Mini Batch SGD Issues 149
6.7.5 Adaptive Gradient Algorithm 149
6.8 Conclusion 151
References 151
7 Privacy Preserving Mechanism by Application of Constrained Nonlinear Optimization Methods in Cyber-Physical System 157
Manas Kumar Yogi and A.S.N. Chakravarthy
7.1 Introduction 157
7.2 Problem Formulation 159
7.3 Proposed Mechanism 160
7.4 Experimental Results 162
7.5 Future Scope 166
7.6 Conclusion 167
References 168
8 Application of Integrated Steganography and Image Compressing Techniques for Confidential Information Transmission 169
Binay Kumar Pandey, Digvijay Pandey, Subodh Wairya, Gaurav Agarwal, Pankaj Dadeech, Sanwta Ram Dogiwal and Sabyasachi Pramanik
8.1 Introduction 170
8.2 Review of Literature 172
8.3 Methodology Used 180
8.4 Results and Discussion 182
8.5 Conclusions 186
References 187
9 Security, Privacy, Risk, and Safety Toward 5G Green Network (5G-GN) 193
Devasis Pradhan, Prasanna Kumar Sahu, Nitin S. Goje, Mangesh M. Ghonge, Hla Myo Tun, Rajeswari R and Sabyasachi Pramanik
9.1 Introduction 194
9.2 Overview of 5G 195
9.3 Key Enabling Techniques for 5G 196
9.4 5G Green Network 200
9.5 5G Technologies: Security and Privacy Issues 202
9.5.1 5G Security Architecture 203
9.5.2 Deployment Security in 5G Green Network 204
9.5.3 Protection of Data Integrity 204
9.5.4 Artificial Intelligence 204
9.6 5G-GN Assets and Threats 205
9.7 5G-GN Security Strategies and Deployments 205
9.8 Risk Analysis of 5G Applications 208
9.9 Countermeasures Against Security and Privacy Risks 209
9.9.1 Enhanced Mobile Broadband 209
9.9.2 Ultra-Reliable Low Latency Communications 209
9.10 Protecting 5G Green Networks Against Attacks 210
9.11 Future Challenges 211
9.12 Conclusion 212
References 213
10 A Novel Cost-Effective Secure Green Data Center Solutions Using Virtualization Technology 217
Subhodip Mukherjee, Debabrata Sarddar, Rajesh Bose and Sandip Roy
10.1 Introduction 218
10.2 Literature Survey 220
10.2.1 Virtualization 220
10.3 Problem Statement 221
10.3.1 VMware Workstation 222
10.4 Green it Using Virtualization 222
10.5 Proposed Work 223
10.5.1 Proposed Secure Virtual Framework 225
10.6 Conclusion 230
Acknowledgments 230
References 230
11 Big Data Architecture for Network Security 233
Dr. Bijender Bansal, V.Nisha Jenipher, Rituraj Jain, Dr. Dilip R., Prof. Makhan Kumbhkar, Sabyasachi Pramanik, Sandip Roy and Ankur Gupta
11.1 Introduction to Big Data 234
11.1.1 10 V's of Big-Data 235
11.1.2 Architecture of Big Data 237
11.1.3 Big Data Access Control 238
11.1.4 Classification of Big Data 239
11.1.4.1 Structured Data 239
11.1.4.2 Unstructured Data 240
11.1.4.3 Semi-Structured Data 240
11.1.5 Need of Big Data 241
11.1.6 Challenges to Big Data Management 241
11.1.7 Big Data Hadoop 242
11.1.8 Big Data Hadoop Architecture 242
11.1.9 Security Factors 242
11.1.10 Performance Factors 243
11.1.11 Security Threats 244
11.1.12 Big Data Security Threats 246
11.1.13 Distributed Data 247
11.1.14 Non-Relational Databases 247
11.1.15 Endpoint Vulnerabilities 247
11.1.16 Data Mining Solutions 248
11.1.17 Access Controls 248
11.1.18 Motivation 249
11.1.19 Importance and Relevance of the Study 250
11.1.20 Background History 250
11.1.21 Research Gaps 252
11.2 Technology Used to Big Data 252
11.2.1 MATLAB 252
11.2.2 Characteristics of MATLAB 253
11.2.3 Research Objectives 253
11.2.4 Methodology 254
11.3 Working Process of Techniques 254
11.3.1 File Splitter 254
11.3.2 GUI Interface for Client 254
11.3.3 GUI Interface for Server 254
11.3.4 Encrypted File 255
11.4 Proposed Work 255
11.4.1 Working 255
11.4.2 Process Flow of Proposed Work 255
11.4.3 Proposed Model 255
11.5 Comparative Analysis 257
11.5.1 Time Comparison 257
11.5.2 Error Rate Comparison 258
11.5.3 Packet Size Comparison 258
11.5.4 Packet Affected Due to Attack 258
11.6 Conclusion and Future Scope 262
11.6.1 Conclusion 262
11.6.2 Future Scope 263
References 264
About the Editors 269
Index 271
