Predictive Data Security using AI: Insights and Issues of Blockchain, IoT, and DevOps

✍ Scribed by Hiren Kumar Thakkar, Mayank Swarnkar, Robin Singh Bhadoria

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 222
• Series: Studies in Computational Intelligence, 1065
• Category: Library

✦ Synopsis

This contributed volume consists of 11 chapters that specifically cover the security aspects of the latest technologies such as Blockchain, IoT, and DevOps, and how to effectively deal with them using Intelligent techniques. Moreover, machine learning (ML) and deep learning (DL) algorithms are also not secured and often manipulated by attackers for data stealing. This book also discusses the types of attacks and offers novel solutions to counter the attacks on ML and DL algorithms. This book describes the concepts and issues with figures and the supporting arguments with facts and charts. In addition to that, the book provides the comparison of different security solutions in terms of experimental results with tables and charts. Besides, the book also provides the future directions for each chapter and novel alternative approaches, wherever applicable. Often the existing literature provides domain-specific knowledge such as the description of security aspects. However, the readers find it difficult to understand how to tackle the application-specific security issues. This book takes one step forward and offers the security issues, current trends, and technologies supported by alternate solutions. Moreover, the book provides thorough guidance on the applicability of ML and DL algorithms to deal with application-specific security issues followed by novel approaches to counter threats to ML and DL algorithms. The book includes contributions from academicians, researchers, security experts, security architectures, and practitioners and provides an in-depth understanding of the mentioned issues.

✦ Table of Contents

Preface
Acknowledgements
Contents
About the Editors
A Comprehensive Study of Security Aspects in Blockchain
1 Introduction
2 Characteristics of Blockchain Technology
3 Working of Blockchain
4 Analysis of Security in Blockchain
4.1 Risks to Blockchain
4.2 Attacks on Blockchain
5 Security Enhancements
6 Applications of Blockchain
7 Trade-Offs and Challenges of Blockchain Technology
8 Conclusion
References
An Exploration Analysis of Social Media Security
1 Introduction to Social Media Security and Its Evolution
2 Important Issues Involving Security for Social Media
2.1 Privacy of Data
2.2 Data Mining
2.3 Virus and Malware Attacks
2.4 Legal Issues
3 Risks and Challenges of Social Media Security
3.1 Information Revelation
3.2 Location Spillage
3.3 Cyberbullying and Cyberstalking
3.4 Cyber Terrorism
3.5 Reputation Misfortune
3.6 Identity Theft
4 Social Media Networks Security Solutions
4.1 Watermarking
4.2 Steganalysis
4.3 Digital Oblivion
4.4 Storage Encryption
4.5 Detection of Malware and Phishing
4.6 Prediction of Cyberattacks Through Monitoring Social Media
4.7 Time Lag-Based Modelling for Software Vulnerability Exploitation Process
4.8 Session Hijacking Counter Measures
4.9 Privacy Set-Up on Social Networking Sites
5 Conclusion
References
A Pragmatic Analysis of Security Concerns in Cloud, Fog, and Edge Environment
1 Introduction to Cloud Computing
2 Introduction to Fog Computing
3 Introduction to Edge Computing
4 Security Threats of Cloud Fog and Edge Computing
5 Potential Solution of Cloud Fog and Edge Computing
6 Conclusion and Future Scope
References
Secure Information and Data Centres: An Exploratory Study
1 Introduction
1.1 History of Data Centre
1.2 Importance of Data Centres in a Business Environment
2 Core Parts of a Data Centre
2.1 Network Infrastructure
2.2 Storage Infrastructure
2.3 Server Infrastructure
2.4 Computing Resources
2.5 Categories of Data Centre Facilities
3 Requirements of a Modern Data Centre
3.1 Abundant, Reliable Power
3.2 Cool Conditions
3.3 Physical and Virtual Security Measures
4 Tiered Data Centres
4.1 Uptime Institute
5 Challenges in Data centre Networking
5.1 Data Security
5.2 Power Management
5.3 Capacity Planning
5.4 The Internet of Things (IoT)
5.5 Mobile Enterprise
5.6 Real-Time Reporting
5.7 Balancing Cost Controls with Efficiency
6 Threats Faced by Data Centres in India
6.1 Inadequate Cognizance of Assets
6.2 Disproportionate Energy Exhaustion
6.3 Inefficient Capacity Planning
6.4 Unfortunate Staff Productivity
6.5 Long Recovery Periods
6.6 Growing Security Concerns
7 Security Threats of Data Centre
7.1 Classes of Data Centre Security
7.2 Who Needs Data Centre Security?
8 Cybersecurity Threats to Heed
8.1 Phishing Engineering Attacks
8.2 Ransomware
8.3 Cyberattacks Against Hosted Services
8.4 IoT-Based Attacks
8.5 Internal Attacks
8.6 Unpatched Security Susceptibility and Bugs
9 How to Keep Data Centre Secure
10 How to Curb These Attacks
10.1 Secure Your Hardware
10.2 Encrypt and Backup Data
10.3 Create a Security-Focused Workplace Culture
10.4 Invest in Cybersecurity Insurance
10.5 Physical Security
10.6 Virtual Security
11 How to Secure Data Centres Against or After Cyberattacks
11.1 Securing Different Regions Through Network Segmentation
11.2 Moving Beyond Segmentation to Cyber
11.3 Advanced Attacks and Mature Attacks
11.4 Behavioural
11.5 Preempt the Silos
12 Checklist to Help with Security Arrangements
13 Benefits of Cybersecurity
14 Conclusion
References
Blockchain-Based Secure E-voting System Using Aadhaar Authentication
1 Introduction
2 Related Work
3 Proposed Work
3.1 System Architecture
4 Implementation Details
5 Security Analysis of Proposed System
6 Comparison with Existing Techniques
7 Conclusion and Future Scope
References
DevOps Tools: Silver Bullet for Software Industry
1 Introduction
1.1 Background
2 DevOps Life Cycle
2.1 Continuous Development
2.2 Continuous Integration
2.3 Continuous Testing
2.4 Continuous Deployment
2.5 Continuous Monitoring
2.6 Continuous Feedback
2.7 Continuous Operations
3 DevOps Tools
3.1 Code
3.2 Build
3.3 Test
3.4 Delivery
3.5 Deployment
3.6 Monitor
4 DevOps in Industry and Education
5 Conclusion and Future Perspective
References
Robust and Secured Reversible Data Hiding Approach for Medical Image Transmission over Smart Healthcare Environment
1 Introduction
2 Related Work
3 Proposed Work
3.1 Watermark Embedding and Extraction
3.2 Watermark Encryption and Decryption
4 Experimental Results and Discussion
4.1 Imperceptibility Test
4.2 Robustness Test
4.3 Security Test
4.4 Computational Cost
5 Conclusions
References
Advancements in Reversible Data Hiding Techniques and Its Applications in Healthcare Sector
1 Introduction
2 Methods of Secure Communication
2.1 Steganography
2.2 Reversible Data Hiding (RDH)
2.3 Digital Watermarking
3 Related Work
3.1 Efficiency Parameters
3.2 Related Works on Reversible Data Hiding
3.3 Related Works on Reversible Watermarking
4 Medical Image Datasets for the Research Work
5 Research Challenges
6 Conclusion
References
Security Issues in Deep Learning
1 Introduction
1.1 Implementations of Deep Learning
2 Background
2.1 Deep Learning
2.2 Deep Neural Networks (DNNs)
2.3 Artificial Intelligence
2.4 DNNs Properties
2.5 Strategies for Secrecy for In-Depth Learning
3 In-Depth Reading of Private Data Frames
3.1 Shokri and Shmatikov
3.2 SecureML
3.3 Google
3.4 CryptoNets
3.5 MiniONN
3.6 Chameleon
3.7 DeepSecure
4 Deep Learning Attack
4.1 Trained Model
4.2 Inputs and Prediction Results
5 Attack that Destroys Example
5.1 Introduction of Model Extraction Attack
5.2 Adversary Model
5.3 Alternative Released Information
6 Possible Attacks of Example
6.1 Introducing the Model Inversion Attack
6.2 Suspected Membership Attack
7 Poison Attack
7.1 Attack Assaults on Ordinary Supervised Analysis (LR)
7.2 Poisoning Assaults in Conventional Unsupervised Learning
7.3 Poison Attack on Deep Learning
7.4 Poison Assault on Strengthening Training
8 Adversarial Attack
8.1 How to Attack Enemies
9 Unlock Problems
10 Conclusion
References
CNN-Based Models for Image Forgery Detection
1 Introduction
2 Theoretical Background
3 Dataset Description
4 Methodology
4.1 Data Pre-processing
4.2 Training Models
4.3 Workflow of the Proposed CNN Model
5 Result and Analysis
5.1 Hyper-parameters
5.2 Pseudocode
5.3 Evaluation Metrics
5.4 Training and Validation Loss Curve
5.5 Confusion Matrix
6 Conclusion and Future Scope
References
Malicious URL Detection Using Machine Learning
1 Introduction
2 Related Work
3 Overview of Principles of Detecting Malicious URLs
3.1 Blacklisting or Heuristic Approaches
3.2 Machine Learning Approaches
4 Datasets
5 Feature Extraction
5.1 URL-Based Lexical Features
5.2 DNS-Based Features
5.3 Webpage Content-Based Features
6 Machine Learning Algorithms for Malicious URL Detection
7 Practical Issues and Open Problems
8 Conclusion
References