Cyber Security and Network Security

✍ Scribed by Sabyasachi Pramanik (editor), Debabrata Samanta (editor), M. Vinay (editor), Abhijit Guha (editor)

• Publisher: Wiley-Scrivener
• Year: 2022
• Tongue: English
• Leaves: 300
• Series: Advances in Cyber Security
• Edition: 1
• Category: Library

✦ Synopsis

CYBER SECURITY AND NETWORK SECURITY

Written and edited by a team of experts in the field, this is the most comprehensive and up-to-date study of the practical applications of cyber security and network security for engineers, scientists, students, and other professionals.

Digital assaults are quickly becoming one of the most predominant issues on the planet. As digital wrongdoing keeps on expanding, it is increasingly more important to investigate new methodologies and advances that help guarantee the security of online networks. Ongoing advances and innovations have made great advances for taking care of security issues in a methodical manner. In light of this, organized security innovations have been delivered so as to guarantee the security of programming and correspondence functionalities at fundamental, improved, and engineering levels.

This outstanding new volume covers all of the latest advances, innovations, and developments in practical applications for cybersecurity and network security. This team of editors represents some of the most well-known and respected experts in the area, creating this comprehensive, up-to-date coverage of the issues of the day and state of the art. Whether for the veteran engineer or scientist or a student, this volume is a must-have for any library.

✦ Table of Contents

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgments
1. Securing Cloud-Based Enterprise Applications and Its Data
1.1 Introduction
1.2 Background and Related Works
1.3 System Design and Architecture
1.3.1 Proposed System Design and Architecture
1.3.2 Modules
1.3.2.1 Compute Instances
1.3.2.2 API Gateway
1.3.2.3 Storage Bucket (Amazon S3)
1.3.2.4 Lambda
1.3.2.5 Load Balancer
1.3.2.6 Internet Gateway
1.3.2.7 Security Groups
1.3.2.8 Autoscaling
1.3.2.9 QLDB
1.3.2.10 NoSQL Database
1.3.2.11 Linux Instance and Networking
1.3.2.12 Virtual Network and Subnet Configuration
1.4 Methodology
1.4.1 Firewall
1.4.2 Malware Injection Prevention
1.4.3 Man-in-the-Middle Prevention
1.4.4 Data at Transit and SSL
1.4.5 Data Encryption at Rest
1.4.6 Centralized Ledger Database
1.4.7 NoSQL Database
1.4.8 Linux Instance and Server Side Installations
1.5 Performance Analysis
1.5.1 Load Balancer
1.5.2 Lambda (For Compression of Data)
1.5.3 Availability Zone
1.5.4 Data in Transit (Encryption)
1.5.5 Data in Rest (Encryption)
1.6 Future Research Direction
1.7 Conclusion
References
2. High-Performance Computing-Based Scalable “Cloud Forensics-as-a-Service” Readiness Framework Factors—A Review
2.1 Introduction
2.2 Aim of the Study
2.3 Motivation for the Study
2.4 Literature Review
2.5 Research Methodology
2.6 Testing Environment Plan
2.7 Testing
2.7.1 Scenario 1: Simultaneous Imaging and Upload and Encryption
2.7.2 Scenario 2: Real-Time Stream Processing
2.7.3 Scenario 3: Remote Desktop Connection, Performance Test
2.8 Recommendations
2.9 Limitations of Present Study
2.10 Conclusions
2.11 Scope for the Future Work
Acknowledgements
References
3 Malware Identification, Analysis and Similarity
3.1 Introduction
3.1.1 Goals of Malware Analysis and Malware Identification
3.1.2 Common Malware Analysis Techniques
3.2 Background and Related Works
3.3 Proposed System Design Architecture
3.3.1 Tool Requirement, System Design, and Architecture
3.3.1.1 For Static Malware Analysis
3.3.1.2 For Dynamic Malware Analysis
3.4 Methodology
3.5 Performance Analysis
3.6 Future Research Direction
3.7 Conclusion
References
4. Robust Fraud Detection Mechanism
4.1 Introduction
4.2 Related Work
4.2.1 Blockchain Technology for Online Business
4.2.2 Validation and Authentication
4.2.3 Types of Online Shopping Fraud
4.2.3.1 Software Fraudulent of Online Shopping
4.2.4 Segmentation/Authentication
4.2.4.1 Secure Transaction Though Segmentation Algorithm
4.2.4.2 Critical Path Segmentation Optimization
4.2.5 Role of Blockchain Technology for Supply Chain and Logistics
4.3 Conclusion
References
5. Blockchain-Based Identity Management Systems
5.1 Introduction
5.2 Preliminaries
5.2.1 Identity Management Systems
5.2.1.1 Identity Factors
5.2.1.2 Architecture of Identity Management Systems
5.2.1.3 Types of Identity Management Systems
5.2.1.4 Importance of Identity Management Systems
5.2.2 Blockchain
5.2.2.1 Blockchain Architecture
5.2.2.2 Components of Blockchain Architecture
5.2.2.3 Merkle Tree
5.2.2.4 Consensus Algorithm
5.2.2.5 Types of Blockchain Architecture
5.2.3 Challenges
5.3 Blockchain-Based Identity Management System
5.3.1 Need for Blockchain-Based Identity Management Systems
5.3.2 Approaches for Blockchain-Based Identity Management Systems
5.3.3 Blockchain-Based Identity Management System Implementations
5.3.4 Impact of Using Blockchain-Based Identity Management on Business and Users
5.3.5 Various Use Cases of Blockchain Identity Management
5.4 Discussion
5.4.1 Challenges Related to Identity
5.4.2 Cost Implications
5.5 Conclusion
5.6 Future Scope
References
6. Insights Into Deep Steganography: A Study of Steganography Automation and Trends
6.1 Introduction
6.2 Convolution Network Learning
6.2.1 CNN Issues
6.3 Recurrent Neural Networks
6.3.1 RNN Forward Propagation
6.4 Long Short-Term Memory Networks
6.4.1 LSTM Issues
6.5 Back Propagation in Neural Networks
6.6 Literature Survey on Neural Networks in Steganography
6.6.1 TS-RNN: Text Steganalysis Based on Recurrent Neural Networks
6.6.2 Generative Text Steganography Based on LSTM Network and Attention Mechanism with Keywords
6.6.3 Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph
6.6.4 RITS: Real-Time Interactive Text Steganography Based on Automatic Dialogue Model
6.6.5 Steganalysis and Payload Estimation of Embedding in Pixel Differences Using Neural Networks
6.6.6 Reversible Data Hiding Using Multilayer Perceptron–Based Pixel Prediction
6.6.7 Neural Network–Based Steganography Algorithm for Still Images
6.7 Optimization Algorithms in Neural Networks
6.7.1 Gradient Descent
6.7.1.1 GD Issues
6.7.2 Stochastic Gradient Descent
6.7.2.1 SGD Issues
6.7.3 SGD with Momentum
6.7.4 Mini Batch SGD
6.7.4.1 Mini Batch SGD Issues
6.7.5 Adaptive Gradient Algorithm
6.8 Conclusion
References
7. Privacy Preserving Mechanism by Application of Constrained Nonlinear Optimization Methods in Cyber-Physical System
7.1 Introduction
7.2 Problem Formulation
7.3 Proposed Mechanism
7.4 Experimental Results
7.5 Future Scope
7.6 Conclusion
References
8. Application of Integrated Steganography and Image Compressing Techniques for Confidential Information Transmission
8.1 Introduction
8.2 Review of Literature
8.3 Methodology Used
8.4 Results and Discussion
8.5 Conclusions
References
9. Security, Privacy, Risk, and Safety Toward 5G Green Network (5G-GN)
9.1 Introduction
9.2 Overview of 5G
9.3 Key Enabling Techniques for 5G
9.4 5G Green Network
9.5 5G Technologies: Security and Privacy Issues
9.5.1 5G Security Architecture
9.5.2 Deployment Security in 5G Green Network
9.5.3 Protection of Data Integrity
9.5.4 Artificial Intelligence
9.6 5G-GN Assets and Threats
9.7 5G-GN Security Strategies and Deployments
9.8 Risk Analysis of 5G Applications
9.9 Countermeasures Against Security and Privacy Risks
9.9.1 Enhanced Mobile Broadband
9.9.2 Ultra-Reliable Low Latency Communications
9.10 Protecting 5G Green Networks Against Attacks
9.11 Future Challenges
9.12 Conclusion
References
10. A Novel Cost-Effective Secure Green Data Center Solutions Using Virtualization Technology
10.1 Introduction
10.2 Literature Survey
10.2.1 Virtualization
10.3 Problem Statement
10.3.1 VMware Workstation
10.4 Green it Using Virtualization
10.5 Proposed Work
10.5.1 Proposed Secure Virtual Framework
10.6 Conclusion
Acknowledgments
References
11. Big Data Architecture for Network Security
11.1 Introduction to Big Data
11.1.1 10 V’s of Big-Data
11.1.2 Architecture of Big Data
11.1.3 Big Data Access Control
11.1.4 Classification of Big Data
11.1.4.1 Structured Data
11.1.4.2 Unstructured Data
11.1.4.3 Semi-Structured Data
11.1.5 Need of Big Data
11.1.6 Challenges to Big Data Management
11.1.7 Big Data Hadoop
11.1.8 Big Data Hadoop Architecture
11.1.9 Security Factors
11.1.10 Performance Factors
11.1.11 Security Threats
11.1.12 Big Data Security Threats
11.1.13 Distributed Data
11.1.14 Non-Relational Databases
11.1.15 Endpoint Vulnerabilities
11.1.16 Data Mining Solutions
11.1.17 Access Controls
11.1.18 Motivation
11.1.19 Importance and Relevance of the Study
11.1.20 Background History
11.1.21 Research Gaps
11.2 Technology Used to Big Data
11.2.1 MATLAB
11.2.2 Characteristics of MATLAB
11.2.3 Research Objectives
11.2.4 Methodology
11.3 Working Process of Techniques
11.3.1 File Splitter
11.3.2 GUI Interface for Client
11.3.3 GUI Interface for Server
11.3.4 Encrypted File
11.4 Proposed Work
11.4.1 Working
11.4.2 Process Flow of Proposed Work
11.4.3 Proposed Model
11.5 Comparative Analysis
11.5.1 Time Comparison
11.5.2 Error Rate Comparison
11.5.3 Packet Size Comparison
11.5.4 Packet Affected Due to Attack
11.6 Conclusion and Future Scope
11.6.1 Conclusion
11.6.2 Future Scope
References
About the Editors
Index
Also of Interest