Cyber Security and Network Security

ISBN-13: 9781119812494 / Angielski / Twarda / 2022 / 320 str.

Sabyasachi Pramanik; Debabrata Samanta; Vinay M

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Preface xvAcknowledgments xxiii1 Securing Cloud-Based Enterprise Applications and Its Data 1Subhradip Debnath, Aniket Das and Budhaditya Sarkar1.1 Introduction 21.2 Background and Related Works 31.3 System Design and Architecture 51.3.1 Proposed System Design and Architecture 51.3.2 Modules 51.3.2.1 Compute Instances 51.3.2.2 API Gateway 61.3.2.3 Storage Bucket (Amazon S3) 61.3.2.4 Lambda 61.3.2.5 Load Balancer 61.3.2.6 Internet Gateway 61.3.2.7 Security Groups 71.3.2.8 Autoscaling 71.3.2.9 QLDB 71.3.2.10 NoSQL Database 81.3.2.11 Linux Instance and Networking 81.3.2.12 Virtual Network and Subnet Configuration 81.4 Methodology 91.4.1 Firewall 91.4.2 Malware Injection Prevention 91.4.3 Man-in-the-Middle Prevention 91.4.4 Data at Transit and SSL 91.4.5 Data Encryption at Rest 101.4.6 Centralized Ledger Database 101.4.7 NoSQL Database 101.4.8 Linux Instance and Server Side Installations 101.5 Performance Analysis 211.5.1 Load Balancer 211.5.2 Lambda (For Compression of Data) 221.5.3 Availability Zone 231.5.4 Data in Transit (Encryption) 231.5.5 Data in Rest (Encryption) 231.6 Future Research Direction 231.7 Conclusion 24References 252 High-Performance Computing-Based Scalable "Cloud Forensicsas- a-Service" Readiness Framework Factors--A Review 27Srinivasa Rao Gundu, Charanarur Panem and S. Satheesh2.1 Introduction 282.2 Aim of the Study 292.3 Motivation for the Study 292.4 Literature Review 302.5 Research Methodology 322.6 Testing Environment Plan 322.7 Testing 352.7.1 Scenario 1: Simultaneous Imaging and Upload and Encryption 362.7.2 Scenario 2: Real-Time Stream Processing 412.7.3 Scenario 3: Remote Desktop Connection, Performance Test 412.8 Recommendations 422.9 Limitations of Present Study 422.10 Conclusions 432.11 Scope for the Future Work 43Acknowledgements 44References 443 Malware Identification, Analysis and Similarity 47Subhradip Debnath and Soumyanil Biswas3.1 Introduction 483.1.1 Goals of Malware Analysis and Malware Identification 483.1.2 Common Malware Analysis Techniques 493.2 Background and Related Works 493.3 Proposed System Design Architecture 513.3.1 Tool Requirement, System Design, and Architecture 513.3.1.1 For Static Malware Analysis 513.3.1.2 For Dynamic Malware Analysis 563.4 Methodology 623.5 Performance Analysis 673.6 Future Research Direction 673.7 Conclusion 68References 684 Robust Fraud Detection Mechanism 71Balajee Maram, Veerraju Gampala, Satish Muppidi and T. Daniya4.1 Introduction 724.2 Related Work 764.2.1 Blockchain Technology for Online Business 764.2.2 Validation and Authentication 794.2.3 Types of Online Shopping Fraud 814.2.3.1 Software Fraudulent of Online Shopping 814.2.4 Segmentation/Authentication 824.2.4.1 Secure Transaction Though Segmentation Algorithm 834.2.4.2 Critical Path Segmentation Optimization 854.2.5 Role of Blockchain Technology for Supply Chain and Logistics 874.3 Conclusion 91References 925 Blockchain-Based Identity Management Systems 95Ramani Selvanambi, Bhavya Taneja, Priyal Agrawal, Henil Jayesh Thakor and Marimuthu Karuppiah5.1 Introduction 965.2 Preliminaries 995.2.1 Identity Management Systems 995.2.1.1 Identity Factors 995.2.1.2 Architecture of Identity Management Systems 995.2.1.3 Types of Identity Management Systems 1005.2.1.4 Importance of Identity Management Systems 1015.2.2 Blockchain 1025.2.2.1 Blockchain Architecture 1025.2.2.2 Components of Blockchain Architecture 1025.2.2.3 Merkle Tree 1035.2.2.4 Consensus Algorithm 1035.2.2.5 Types of Blockchain Architecture 1055.2.3 Challenges 1065.3 Blockchain-Based Identity Management System 1095.3.1 Need for Blockchain-Based Identity Management Systems 1095.3.2 Approaches for Blockchain-Based Identity Management Systems 1105.3.3 Blockchain-Based Identity Management System Implementations 1115.3.4 Impact of Using Blockchain-Based Identity Management on Business and Users 1205.3.5 Various Use Cases of Blockchain Identity Management 1215.4 Discussion 1225.4.1 Challenges Related to Identity 1225.4.2 Cost Implications 1235.5 Conclusion 1235.6 Future Scope 124References 1256 Insights Into Deep Steganography: A Study of Steganography Automation and Trends 129R. Gurunath, Debabrata Samanta and Digvijay Pandey6.1 Introduction 1306.2 Convolution Network Learning 1316.2.1 CNN Issues 1326.3 Recurrent Neural Networks 1336.3.1 RNN Forward Propagation 1356.4 Long Short-Term Memory Networks 1366.4.1 LSTM Issues 1376.5 Back Propagation in Neural Networks 1386.6 Literature Survey on Neural Networks in Steganography 1406.6.1 TS-RNN: Text Steganalysis Based on Recurrent Neural Networks 1406.6.2 Generative Text Steganography Based on LSTM Network and Attention Mechanism with Keywords 1416.6.3 Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph 1426.6.4 RITS: Real-Time Interactive Text Steganography Based on Automatic Dialogue Model 1436.6.5 Steganalysis and Payload Estimation of Embedding in Pixel Differences Using Neural Networks 1446.6.6 Reversible Data Hiding Using Multilayer Perceptron-Based Pixel Prediction 1446.6.7 Neural Network-Based Steganography Algorithm for Still Images 1456.7 Optimization Algorithms in Neural Networks 1456.7.1 Gradient Descent 1456.7.1.1 GD Issues 1466.7.2 Stochastic Gradient Descent 1476.7.2.1 SGD Issues 1486.7.3 SGD with Momentum 1486.7.4 Mini Batch SGD 1496.7.4.1 Mini Batch SGD Issues 1496.7.5 Adaptive Gradient Algorithm 1496.8 Conclusion 151References 1517 Privacy Preserving Mechanism by Application of Constrained Nonlinear Optimization Methods in Cyber-Physical System 157Manas Kumar Yogi and A.S.N. Chakravarthy7.1 Introduction 1577.2 Problem Formulation 1597.3 Proposed Mechanism 1607.4 Experimental Results 1627.5 Future Scope 1667.6 Conclusion 167References 1688 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 Pramanik8.1 Introduction 1708.2 Review of Literature 1728.3 Methodology Used 1808.4 Results and Discussion 1828.5 Conclusions 186References 1879 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 Pramanik9.1 Introduction 1949.2 Overview of 5G 1959.3 Key Enabling Techniques for 5G 1969.4 5G Green Network 2009.5 5G Technologies: Security and Privacy Issues 2029.5.1 5G Security Architecture 2039.5.2 Deployment Security in 5G Green Network 2049.5.3 Protection of Data Integrity 2049.5.4 Artificial Intelligence 2049.6 5G-GN Assets and Threats 2059.7 5G-GN Security Strategies and Deployments 2059.8 Risk Analysis of 5G Applications 2089.9 Countermeasures Against Security and Privacy Risks 2099.9.1 Enhanced Mobile Broadband 2099.9.2 Ultra-Reliable Low Latency Communications 2099.10 Protecting 5G Green Networks Against Attacks 2109.11 Future Challenges 2119.12 Conclusion 212References 21310 A Novel Cost-Effective Secure Green Data Center Solutions Using Virtualization Technology 217Subhodip Mukherjee, Debabrata Sarddar, Rajesh Bose and Sandip Roy10.1 Introduction 21810.2 Literature Survey 22010.2.1 Virtualization 22010.3 Problem Statement 22110.3.1 VMware Workstation 22210.4 Green it Using Virtualization 22210.5 Proposed Work 22310.5.1 Proposed Secure Virtual Framework 22510.6 Conclusion 230Acknowledgments 230References 23011 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 Gupta11.1 Introduction to Big Data 23411.1.1 10 V's of Big-Data 23511.1.2 Architecture of Big Data 23711.1.3 Big Data Access Control 23811.1.4 Classification of Big Data 23911.1.4.1 Structured Data 23911.1.4.2 Unstructured Data 24011.1.4.3 Semi-Structured Data 24011.1.5 Need of Big Data 24111.1.6 Challenges to Big Data Management 24111.1.7 Big Data Hadoop 24211.1.8 Big Data Hadoop Architecture 24211.1.9 Security Factors 24211.1.10 Performance Factors 24311.1.11 Security Threats 24411.1.12 Big Data Security Threats 24611.1.13 Distributed Data 24711.1.14 Non-Relational Databases 24711.1.15 Endpoint Vulnerabilities 24711.1.16 Data Mining Solutions 24811.1.17 Access Controls 24811.1.18 Motivation 24911.1.19 Importance and Relevance of the Study 25011.1.20 Background History 25011.1.21 Research Gaps 25211.2 Technology Used to Big Data 25211.2.1 MATLAB 25211.2.2 Characteristics of MATLAB 25311.2.3 Research Objectives 25311.2.4 Methodology 25411.3 Working Process of Techniques 25411.3.1 File Splitter 25411.3.2 GUI Interface for Client 25411.3.3 GUI Interface for Server 25411.3.4 Encrypted File 25511.4 Proposed Work 25511.4.1 Working 25511.4.2 Process Flow of Proposed Work 25511.4.3 Proposed Model 25511.5 Comparative Analysis 25711.5.1 Time Comparison 25711.5.2 Error Rate Comparison 25811.5.3 Packet Size Comparison 25811.5.4 Packet Affected Due to Attack 25811.6 Conclusion and Future Scope 26211.6.1 Conclusion 26211.6.2 Future Scope 263References 264About the Editors 269Index 271

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Sabyasachi Pramanik 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.Debabrata Samanta, PhD, 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.M. Vinay, PhD, 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.Abhijit Guha 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.