Automated Secure Computing for Next-Generation Systems

• Autore: Tyagi Amit Kumar
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (468 pages)
• Disciplina: 005.8
• Soggetto topico: Computer security
• Note generali: Description based upon print version of record.; Chapter 4 Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications for Next-Generation Society
• Nota di contenuto: 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.
• Sommario/riassunto: 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 frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user's privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. Audience Researchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students.
• Titolo autorizzato: Automated Secure Computing for Next-Generation Systems
• ISBN: 9781394213948; 1394213948; 9781394213924; 1394213921
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911019455103321
• Lo trovi qui: Univ. Federico II