This reference reviews the architectural requirements of IT systems that are designed to digitally transform business operations. It is a compilation of 7 timely reviews that demonstrate how adopting emerging technologies and examining the security-based concerns can lead to innovation in the business sector. The aim of the book is to guide scholars and business consultants on IT and business frameworks that can help new and existing organizations navigate the challenges posed by disruptive technologies to create a competitive advantage. The reviews are contributed by experts in business and information technology.
The chapters cover diverse topics related to technological advancements and digital security measures. Chapter 1 offers insights into accessing and securing patient medical records through a blockchain-based framework, detailing research methodology, scalability, and standards. Chapter 2 discusses cyber threats in IoT-connected cars, addressing vulnerabilities, attack methods, and defense strategies. Chapter 3 focuses on malware analysis and detection using machine learning techniques. Chapter 4 emphasizes on securing IoT-based home automation. Chapter 5 presents an IoT policy and governance reference architecture to ensure integrity and security across devices. Chapter 6 explores organizational security improvements to prevent deepfake ransomware. Finally, Chapter 7 examines the use of machine learning in credit card fraud detection, discussing challenges and control layers.
The chapters cover diverse topics related to technological advancements and digital security measures. Chapter 1 offers insights into accessing and securing patient medical records through a blockchain-based framework, detailing research methodology, scalability, and standards. Chapter 2 discusses cyber threats in IoT-connected cars, addressing vulnerabilities, attack methods, and defense strategies. Chapter 3 focuses on malware analysis and detection using machine learning techniques. Chapter 4 emphasizes on securing IoT-based home automation. Chapter 5 presents an IoT policy and governance reference architecture to ensure integrity and security across devices. Chapter 6 explores organizational security improvements to prevent deepfake ransomware. Finally, Chapter 7 examines the use of machine learning in credit card fraud detection, discussing challenges and control layers.
Readership
Scholars, academics, business consultants, entrepreneurs.Table of Contents
Contents- Foreword
- Preface
- List of Contributors
- Daniel Mago Vistro, Muhammad Shoaib Farooq, Attique Ur Rehman and Waleed
- Zafar
- 1. Introduction
- 2. Related Work
- 3. Research Methodology
- 4. Research Objective
- 5. Research Question
- 6. Conducting Search
- 7. Inclusion and Exclusion Criteria
- 8. Search and Results and Collection
- 9. Keywording
- 10. Quality Assessment
- 11. Data Extraction and Classification
- 12. Assessments of Research Questions
- 13. Scalability
- 14. Standards
- 15. Transformation
- 16. Complexity Discussion
- Conclusion
- References
- Ainkaran Doraisamy, Nor Azlina Abdul Rahman and Khalida Shajaratuddur Harun 14
- 1. Introduction
- 2. Vulnerabilities
- 3. Methods of Attacks
- 3.1. Key Fob
- 3.2. Attack Via Tesla App in Android Smartphone
- 3.3. Attack Via Infotainment System
- 3.4. Gps Spoofing Attack
- 4. Methods of Defense
- 4.1. Key Fob Hack
- 4.2. Tesla App
- 4.3. Infotainment System
- 4.4. Gps Spoof
- 5. Conceptual Framework
- Conclusion
- References
- Muhammad Jawed Chowdhury, Julia Juremi and Maryam Var Naseri
- 1. Introduction
- 2. Aiva System Architecture
- 2.1. Aiva Core Components
- 2.1.1. Static Analysis
- 2.1.2. Supervised Machine Learning
- 2.1.3. Virustotal Application Programming Interface (Api)
- 3. Results and Discussions
- Conclusion
- References
- Abdullah Khalid, Nor Azlina Abdul Rahman and Khalida Shajaratuddur Harun
- 1. Introduction
- 2. Smart Home Iot
- 2.1. Smart Homes Architecture
- 3. Security Vulnerabilities, Threats and Risks
- 3.1. Vulnerabilities & Threats
- 3.2. Weak Credentials
- 3.3. Insecure Network Services/Hardware Exploitation
- 3.4. Internal Device Failures/Limitations
- 3.5. Insecure Ecosystem Interfaces
- 3.6. Inefficient Update Mechanisms and Insecure Components
- 3.7. Risks
- 4. Business Continuity and Disaster Recovery
- 5. Organizational Security, Awareness and Information Sharing
- Conclusion
- References
- Integrity and Security of Information Across IoT Devices
- Yap Chi Yew, Intan Farahana Kamsin and Nur Khairunnisha Zainal
- 1. Introduction
- 2. Reference Architecture of Iot
- 3. IoT Policy
- 4. IoT Governance
- 5. Framework (Identify, Insulate, Inspect and Improve Framework) 56
- Conclusion
- References
- Janesh Kapoor and Nor Azlina Abdul Rahman
- 1. Introduction
- 1.1. What is Deepfake?
- 1.2. How Deepfakes Are Created?
- 1.3. Why Deepfakes Were Created?
- 2. Deepfake Ransomware Impact and Potential Risk to The
- Organization
- 2.1. Customer’S Trust and Confidence
- 2.2. Social Engineering
- 2.3. C-Level Fraud
- 2.4. Extortion Against Influential Business Leaders
- 2.5. Tarnish Organisation's Reputation
- 2.6. Operational Impact
- 2.7. Market Stock Manipulation
- 2.8. The Financial Burden
- 2.9. Server Message Block (Smb)
- 3. Risk Management, Business Continuity and Disaster Recovery
- Taken by the Organisation to Handle the Situation
- 3.1. Risk Management
- 3.2. Business Continuity
- 3.2.1. Project Management (Pm)
- 3.2.2. Risk Analysis and Review (Rar)
- 3.2.3. Business Impact Analysis (Bia)
- 3.2.4. Business Continuity Strategy (Bcs)
- 3.2.5. Business Continuity Planning Process
- 3.2.6. Testing, Exercising, and Improving
- 3.2.7. Program Management
- 3.3. Disaster Recovery
- 3.3.1. Disaster Recovery Team
- 3.3.2. Review Emergency Kit
- 3.3.3. Review Contact List
- 3.3.4. Identifying Alternative Suppliers and Facilities
- 3.3.5. Includes Business Impact Analysis
- 3.3.6. Inventory Check List of Physical Assets
- 3.3.7. Inventory Check List of Logical Assets
- 3.3.8. Communication Plan
- 3.3.9. Data Backup Plans
- 3.3.10. Testing the Disaster Recovery Plan
- 3.3.11. Ai in Disaster Recovery
- 4. Defence Techniques
- 4.1. Comprehensive Data Backup and Recovery Plan
- 4.2. Inconsistencies
- 4.3. Limitation of Voice and Images
- 4.4. Multi-Factor Authentication
- 4.5. Restriction of Deepfake Tools
- 4.6. Isolate Infected Devices
- 4.7. Intrusion Prevention Software
- 4.8. Ai and Blockchain Detection Technology
- 4.9. Content Authenticity Initiative (Cai)
- 4.10. Systems and Software Updates
- 4.11. Enable Anti-Virus
- 4.12. Server Message Block (Smb)
- 5. Awareness of the Deepfakes Ransomware
- 5.1. Email Scams
- 5.2. Malware
- 5.3. Password Security
- 5.4. Removable Media
- 5.5. Safe Internet Habits
- 5.6. Data Management and Privacy
- 5.7. Inconsistencies
- 5.8. Security Protocols Act
- 5.9. Considering the Source
- Conclusion
- References
- Manoj Jayabalan and Shiksha
- 1. Introduction
- 2. Control Layers in Credit Card Fraud Detection System
- 3. Types of Credit Card Fraud Detection System
- 3.1. Machine Learning Techniques
- 3.2. Selection of Suitable Techniques for Credit Card Fraud Detection
- 4. Credit Card Fraud Detection Challenges
- 5. Discussion
- Conclusion
- References
- Subject Index
Author
- Muhammad Ehsan Rana
- Manoj Jayabalan
