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

<p><b>Sabyasachi Pramanik</b> is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He earned his PhD in computer science and engineering from the Sri Satya Sai University of Technology and Medical Sciences, Bhopal, India. He has more than 50 publications in various scientific and technical conferences, journals, and online book chapter contributions. He is also serving as the editorial board member on many scholarly journals and has authored one book. He is an editor of various books from a number of publishers, including Scrivener Publishing. <p><b>Debabrata Samanta, PhD,</b> is an assistant professor in the Department of Computer Science, Christ University, Bangalore, India. He obtained his PhD in from the National Institute of Technology, Durgapur, India, and he is the owner of 20 patents and two copyrights. He has authored or coauthored over 166 research papers in international journals and conferences and has received the “Scholastic Award” at the Second International Conference on Computer Science and IT application in Delhi, India. He is a co-author of 11 books and the co-editor of seven books and has presented various papers at international conferences and received Best Paper awards. He has authored o co-authored 20 Book Chapters. <p><b>M. Vinay, PhD,</b> obtained his PhD at JJT University Rajasthan for Computer Science and is an assistant professor of computer science at Christ University, Bengaluru, India. With over 14 years of teaching, he has received numerous prestigious teaching awards. He has given more than 30 invited talks, 35 guests lectures and conducted more than 25 workshops, He has also published over a dozen papers in distinguished scholarly journals. <p><b>Abhijit Guha</b> is pursuing a doctorate with the Department of Data Science, Christ University, India. He is currently working as a research and development scientist with First American India Private Ltd. He received three consecutive “Innovation of the Year” awards, from 2015 to 2017, by First American India for his contribution towards his research.

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