Preface xv Acknowledgments xxiii 1 Securing Cloud-Based Enterprise Applications and Its Data 1Subhradip 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 27Srinivasa 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 47Subhradip 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 71Balajee 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 95Ramani 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 129R. 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 157Manas 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 169Binay 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) 193Devasis 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 217Subhodip 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 233Dr. 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

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