Automated Secure Computing for Next-Generation Systems

Automated Secure Computing for Next-Generation Systems

AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMS This book provides cutting-edge chapters on machine-empowered solutions for next-generation systems for today's society. Security is always a primary concern for each application and sector. In the last decade, many techniques and framework...

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Detalles Bibliográficos
Autor principal: Tyagi, Amit Kumar (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Materias:
Computer security.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811332006719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Acknowledgements
  • Part 1: Fundamentals
  • Chapter 1 Digital Twin Technology: Necessity of the Future in Education and Beyond
  • 1.1 Introduction
  • 1.2 Digital Twins in Education
  • 1.2.1 Virtual Reality for Immersive Learning
  • 1.2.2 Delivery of Remote Education
  • 1.2.3 Replication of Real-World Scenarios
  • 1.2.4 Promote Intelligences and Personalization
  • 1.3 Examples and Case Studies
  • 1.3.1 Examples of DTT in Education
  • 1.3.2 Digital Twin-Based Educational Systems
  • 1.4 Discussion
  • 1.5 Challenges and Limitations
  • 1.5.1 Technical Challenges
  • 1.5.2 Pedagogical Challenges
  • 1.5.3 Ethical and Privacy Concerns
  • 1.5.4 Future Research Directions
  • 1.6 Conclusion
  • References
  • Chapter 2 An Intersection Between Machine Learning, Security, and Privacy
  • 2.1 Introduction
  • 2.2 Machine Learning
  • 2.2.1 Overview of Machine Learning
  • 2.2.2 Machine Learning Stages: Training and Inference
  • 2.3 Threat Model
  • 2.3.1 Attack Model of Machine Learning
  • 2.3.2 Trust Model
  • 2.3.3 Machine Learning Capabilities in a Differential Environment
  • 2.3.4 Opposite Views of Machine Learning in Security
  • 2.4 Training in a Differential Environment
  • 2.4.1 Achieving Integrity
  • 2.5 Inferring in Adversarial Attack
  • 2.5.1 Combatants in the White Box Model
  • 2.5.2 Insurgencies in the Black Box Model
  • 2.6 Machine Learning Methods That Are Sustainable, Private, and Accountable
  • 2.6.1 Robustness of Models to Distribution Drifts
  • 2.6.2 Learning and Inferring With Privacy
  • 2.6.3 Fairness and Accountability in Machine Learning
  • 2.7 Conclusion
  • References
  • Chapter 3 Decentralized, Distributed Computing for Internet of Things-Based Cloud Applications
  • 3.1 Introduction to Volunteer Edge Cloud for Internet of Things Utilising Blockchain.
  • 3.2 Significance of Volunteer Edge Cloud Concept
  • 3.3 Proposed System
  • 3.3.1 Smart Contract
  • 3.3.2 Order Task Method
  • 3.3.3 KubeEdge
  • 3.4 Implementation of Volunteer Edge Control
  • 3.4.1 Formation of a Cloud Environment
  • 3.5 Result Analysis of Volunteer Edge Cloud
  • 3.6 Introducing Blockchain-Enabled Internet of Things Systems Using the Serverless Cloud Platform
  • 3.7 Introducing Serverless Cloud Platforms
  • 3.7.1 IoT Systems
  • 3.7.2 JointCloud
  • 3.7.3 Computing Without Servers
  • 3.7.4 Oracle and Blockchain Technology
  • 3.8 Serverless Cloud Platform System Design
  • 3.8.1 Aim and Constraints
  • 3.8.2 Goals and Challenges
  • 3.8.3 HCloud Connections
  • 3.8.4 Data Sharing Platform
  • 3.8.5 Cloud Manager
  • 3.8.6 The Agent
  • 3.8.7 Client Library
  • 3.8.8 Witness Blockchain
  • 3.9 Evaluation of HCloud
  • 3.9.1 CPU Utilization
  • 3.9.2 Cost Analysis
  • 3.10 HCloud-Related Works
  • 3.10.1 Serverless
  • 3.10.2 Efficiency
  • 3.11 Conclusion
  • References
  • Chapter 4 Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications for Next-Generation Society
  • 4.1 Introduction
  • 4.2 Background Work
  • 4.3 Motivation
  • 4.4 Existing Innovations in the Current Society
  • 4.5 Expected Innovations in the Next-Generation Society
  • 4.6 An Environment with Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications
  • 4.7 Open Issues in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications
  • 4.8 Research Challenges in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications
  • 4.9 Legal Challenges in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications
  • 4.10 Future Research Opportunities Towards Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications.
  • 4.11 An Open Discussion
  • 4.12 Conclusion
  • References
  • Chapter 5 Artificial Intelligence for Cyber Security: Current Trends and Future Challenges
  • 5.1 Introduction: Security and Its Types
  • 5.1.1 Human Aspects of Information Security
  • 5.2 Network and Information Security for Industry 4.0 and Society 5.0
  • 5.2.1 Industry 4.0 vs Society 5.0
  • 5.2.2 Industry 4.0 to Society 5.0
  • 5.3 Internet Monitoring, Espionage, and Surveillance
  • 5.4 Cyber Forensics with Artificial Intelligence and without Artificial Intelligence
  • 5.5 Intrusion Detection and Prevention Systems Using Artificial Intelligence
  • 5.6 Homomorphic Encryption and Cryptographic Obfuscation
  • 5.7 Artificial Intelligence Security as Adversarial Machine Learning
  • 5.8 Post-Quantum Cryptography
  • 5.9 Security and Privacy in Online Social Networks and Other Sectors
  • 5.10 Security and Privacy Using Artificial Intelligence in Future Applications/Smart Applications
  • 5.11 Security Management and Security Operations Using Artificial Intelligence for Society 5.0 and Industry 4.0
  • 5.11.1 Implementation on the Internet of Things and Protecting Data in IoT Connected Devices
  • 5.12 Digital Trust and Reputation Using Artificial Intelligence
  • 5.13 Human-Centric Cyber Security Solutions
  • 5.14 Artificial Intelligence-Based Cyber Security Technologies and Solutions
  • 5.15 Open Issues, Challenges, and New Horizons Towards Artificial Intelligence and Cyber Security
  • 5.15.1 An Overview of Cyber-Security
  • 5.15.2 The Role of Artificial Intelligence in Cyber Security
  • 5.15.3 AI Is Continually Made Smarter
  • 5.15.4 AI Never Misses a Day of Work
  • 5.15.5 AI Swiftly Spots the Threats
  • 5.15.6 Impact of AI on Cyber Security
  • 5.15.7 AI in Cyber Security Case Study
  • 5.16 Future Research with Artificial Intelligence and Cyber Security
  • 5.17 Conclusion
  • References.
  • Part 2: Methods and Techniques
  • Chapter 6 An Automatic Artificial Intelligence System for Malware Detection
  • 6.1 Introduction
  • 6.2 Malware Types
  • 6.3 Structure Format of Binary Executable Files
  • 6.4 Malware Analysis and Detection
  • 6.5 Malware Techniques to Evade Analysis and Detection
  • 6.6 Malware Detection With Applying AI
  • 6.7 Open Issues and Challenges
  • 6.8 Discussion and Conclusion
  • References
  • Chapter 7 Early Detection of Darknet Traffic in Internet of Things Applications
  • 7.1 Introduction
  • 7.2 Literature Survey
  • 7.3 Proposed Work
  • 7.3.1 Drawback
  • 7.4 Analysis of the Work
  • 7.5 Future Work
  • 7.6 Conclusion
  • References
  • Chapter 8 A Novel and Efficient Approach to Detect Vehicle Insurance Claim Fraud Using Machine Learning Techniques
  • 8.1 Introduction
  • 8.2 Literature Survey
  • 8.3 Implementation and Analysis
  • 8.3.1 Dataset Description
  • 8.3.2 Methodology
  • 8.3.3 Checking for Missing Values
  • 8.3.4 Exploratory Data Analysis
  • 8.4 Conclusion
  • 8.4.1 Future Work
  • 8.4.2 Limitations
  • References
  • Chapter 9 Automated Secure Computing for Fraud Detection in Financial Transactions
  • 9.1 Introduction
  • 9.2 Historical Perspective
  • 9.3 Previous Models for Fraud Detection in Financial Transactions
  • 9.3.1 CatBoost
  • 9.3.2 XGBoost
  • 9.3.3 LightGBM
  • 9.4 Proposed Model Based on Automated Secure Computing
  • 9.5 Discussion
  • 9.6 Conclusion
  • References
  • Additional Readings
  • Chapter 10 Data Anonymization on Biometric Security Using Iris Recognition Technology
  • 10.1 Introduction
  • 10.2 Problems Faced in Facial Recognition
  • 10.3 Face Recognition
  • 10.4 The Important Aspects of Facial Recognition
  • 10.5 Proposed Methodology
  • 10.6 Results and Discussion
  • 10.7 Conclusion
  • References
  • Chapter 11 Analysis of Data Anonymization Techniques in Biometric Authentication System.
  • 11.1 Introduction
  • 11.2 Literature Survey
  • 11.3 Existing Survey
  • 11.3.1 Biometrics Technology
  • 11.3.2 Palm Vein Authentication
  • 11.3.3 Methods of Palm Vein Authentication
  • 11.3.4 Limitations of the Existing System
  • 11.4 Proposed System
  • 11.4.1 Biometric System
  • 11.4.2 Data Processing Technique
  • 11.4.3 Data-Preserving Approach
  • 11.4.3.1 Generalization
  • 11.4.3.2 Suppression
  • 11.4.3.3 Swapping
  • 11.4.3.4 Masking
  • 11.5 Implementation of AI
  • 11.6 Limitations and Future Works
  • 11.7 Conclusion
  • References
  • Part 3: Applications
  • Chapter 12 Detection of Bank Fraud Using Machine Learning Techniques
  • 12.1 Introduction
  • 12.2 Literature Review
  • 12.3 Problem Description
  • 12.4 Implementation and Analysis
  • 12.4.1 Workflow
  • 12.4.2 Dataset
  • 12.4.3 Methodology
  • 12.5 Results
  • 12.6 Conclusion
  • 12.7 Future Works
  • References
  • Chapter 13 An Internet of Things-Integrated Home Automation with Smart Security System
  • 13.1 Introduction
  • 13.2 Literature Review
  • 13.3 Methodology and Working Procedure with Diagrams
  • 13.4 Research Analysis
  • 13.5 Establishment of the Prototype
  • 13.6 Results and Discussions
  • 13.7 Conclusions
  • Acknowledgment
  • References
  • Chapter 14 An Automated Home Security System Using Secure Message Queue Telemetry Transport Protocol
  • 14.1 Introduction
  • 14.2 Related Works
  • 14.2.1 PIR Home Security Solutions
  • 14.2.2 Solutions for MQTT Security
  • 14.2.3 Solutions for Home Automation
  • 14.3 Proposed Solution
  • 14.3.1 Technological Decisions
  • 14.3.2 Hardware Decision
  • 14.3.3 Module Overview
  • 14.4 Implementation
  • 14.5 Results
  • 14.6 Conclusion and Future Work
  • References
  • Chapter 15 Machine Learning-Based Solutions for Internet of Things-Based Applications
  • 15.1 Introduction
  • 15.2 IoT Ecosystem
  • 15.2.1 IoT Devices
  • 15.2.2 IoT Gateways
  • 15.2.3 IoT Platforms.
  • 15.2.4 IoT Applications.

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