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For Authors
Submit a ProposalAutomated Secure Computing for Next-Generation Systems
 | Edited by Amit Kumar Tyagi
Copyright: 2023 | Status: Published ISBN: 9781394213597 | Hardcover | 480 pages Price: $225 USD  |
One Line Description
This book provides cutting-edge chapters on machine-empowered
solutions for next-generation systems for today’s 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.
Researchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students.
Description
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.
Back to Top
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.
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Author / Editor Details
Amit Kumar Tyagi, PhD, is an assistant professor, at the National Institute of Fashion Technology, New Delhi, India. He has published more than 100 papers in refereed international journals, conferences, and books. He has filed more than 20 national and international patents in the areas of deep learning, Internet of Things, cyber-physical systems, and computer vision. His current research focuses on smart and secure computing and privacy, amongst other interests.
Amit Kumar Tyagi, PhD, is an assistant professor, at the National Institute of Fashion Technology, New Delhi, India. He has published more than 100 papers in refereed international journals, conferences, and books. He has filed more than 20 national and international patents in the areas of deep learning, Internet of Things, cyber-physical systems, and computer vision. His current research focuses on smart and secure computing and privacy, amongst other interests.
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Table of Contents
Preface
Acknowledgements
Part 1: Fundamentals
1. Digital Twin Technology: Necessity of the Future in Education and Beyond
Robertas Damaševičius and Ligita Zailskaitė-Jakštė
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
2. An Intersection Between Machine Learning, Security, and Privacy
Hareharan P.K., Kanishka J. and Subaasri D.
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
3. Decentralized, Distributed Computing for Internet of Things-Based Cloud
Applications
Roopa Devi E.M., Shanthakumari R., Rajadevi R., Kayethri D. and Aparna V.
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
4. Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications for Next-Generation Society
V. Hemamalini, Anand Kumar Mishra, Amit Kumar Tyagi and Vijayalakshmi Kakulapati
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
5. Artificial Intelligence for Cyber Security: Current Trends and Future Challenges
Meghna Manoj Nair, Atharva Deshmukh and Amit Kumar Tyagi
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
6. An Automatic Artificial Intelligence System for Malware Detection
Ahmad Moawad, Ahmed Ismail Ebada, A.A. El-Harby and Aya M. Al-Zoghby
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
7. Early Detection of Darknet Traffic in Internet of Things Applications
Ambika N.
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
8. A Novel and Efficient Approach to Detect Vehicle Insurance Claim Fraud Using Machine Learning Techniques
Anand Kumar Mishra, V. Hemamalini, Amit Kumar Tyagi, Piyali Saha and Abirami A.
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
9. Automated Secure Computing for Fraud Detection in Financial Transactions
Kuldeep Singh, Prasanna Kolar, Rebecca Abraham, Vedantam Seetharam, Sireesha Nanduri and Divyesh Kumar
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
10. Data Anonymization on Biometric Security Using Iris Recognition Technology
Aparna D. K., Malarkodi M., Lakshmanaprakash S., Priya R. L. and Ajay Nair
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
11. Analysis of Data Anonymization Techniques in Biometric Authentication System
Harini S., Dharshini R., Agalya N., Priya R. L. and Ajay Nair
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
12. Detection of Bank Fraud Using Machine Learning Techniques
Kalyani G., Anand Kumar Mishra, Diya Harish, Amit Kumar Tyagi,bSajidha S. A. and Shashank Pandey
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
13. An Internet of Things-Integrated Home Automation with Smart Security System
Md. Sayeduzzaman, Touhidul Hasan, Adel A. Nasser and Akashdeep Negi
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
14. An Automated Home Security System Using Secure Message Queue
Telemetry Transport Protocol
P. Rukmani, S. Graceline Jasmine, M. Vergin Raja Sarobin, L. Jani Anbarasi and Soumitro Datta
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
15. Machine Learning-Based Solutions for Internet of Things-Based Applications
Varsha Bhatia and Bhavesh Bhatia
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
15.2.5 IoT Connectivity
15.2.6 Analytics and Data Management
15.2.7 Security and Privacy
15.2.8 Infrastructure
15.3 Importance of Data in IoT Applications
15.3.1 Data Gathered from IoT Applications
15.3.2 Quality of an IoT Application
15.3.3 Effective IoT Data Utilization
15.4 Machine Learning
15.4.1 Supervised Learning
15.4.2 Unsupervised Learning
15.4.3 Reinforcement Learning
15.5 Machine Learning Algorithms
15.5.1 k-Nearest Neighbors
15.5.2 Logistic Regression
15.5.3 Decision Tree
15.5.4 Random Forest
15.5.5 Support Vector Machines
15.5.6 Artificial Neural Networks
15.5.7 Long Short-Term Memory
15.6 Applications of Machine Learning in IoT
15.6.1 Smart City
15.6.2 Smart Agriculture
15.6.3 Smart Transportation
15.6.4 Smart Grid
15.6.5 Application in Supply Chain Management
15.6.6 Application in Wearable
15.6.7 Applications in Smart Factories
15.7 Challenges of Implementing ML for IoT Solutions
15.7.1 Privacy and Security
15.7.2 Scalability
15.7.3 Lack of Data
15.7.4 Data Quality
15.7.5 Interpretability
15.8 Emerging Trends in IoT
15.8.1 Edge Computing
15.8.2 5G and IoT
15.8.3 Artificial Intelligence and Machine Learning
15.8.4 Security
15.8.5 Blockchain
15.8.6 IoT and Cloud Computing
15.9 Conclusion
References
16. Machine Learning-Based Intelligent Power Systems
Kusumika Krori Dutta, S. Poornima, R. Subha, Lipika Deka and Archit Kamath
16.1 Introduction
16.2 Machine Learning Techniques
16.2.1 Classification Algorithm
16.2.1.1 K-Nearest Neighbor
16.2.1.2 Support Vector Machines
16.2.1.3 Decision Tree
16.2.1.4 Ensemble Boosted Trees
16.2.1.5 Random Forest
16.2.1.6 Naïve Bayes
16.2.1.7 Logistic Regression
16.2.2 Regression Analysis
16.2.2.1 Linear Regression
16.2.2.2 Regression Tree Ensemble
16.2.2.3 Elastic Net Regression
16.2.2.4 Gaussian Process Regression
16.2.2.5 Artificial Neural Networks
16.2.3 Deep Learning Techniques
16.2.3.1 Convolutional Neural Networks
16.2.3.2 Recurrent Neural Networks
16.2.4 Reinforcement Learning
16.3 Implementation of ML Techniques in Smart Power Systems
16.3.1 Fault Detection and Diagnosis
16.3.2 Load Forecasting
16.3.3 Load Disaggregation
16.3.4 Scheduling of Load
16.3.5 Energy Management
16.3.6 Asset Monitoring
16.4 Case Study
16.5 Conclusion
Further Reading
References
Part 4: Future Research Opportunities
17. Quantum Computation, Quantum Information, and Quantum Key Distribution
Mohanaprabhu D., Monish Kanna S. P., Jayasuriya J., Lakshmanaprakash S., Abirami A. and Amit Kumar Tyagi
17.1 Introduction
17.2 Literature Work
17.3 Motivation Behind this Study
17.4 Existing Players in the Market
17.5 Quantum Key Distribution
17.6 Proposed Models for Quantum Computing
17.7 Simulation/Result
17.7.1 Issues and Challenges in Quantum Computing
17.7.2 Issues in Quantum Key Distribution
17.7.3 The Future Ahead With Quantum Computation, Quantum Information, and Quantum Key Distribution
17.8 Conclusion
References
18. Quantum Computing, Qubits with Artificial Intelligence, and Blockchain Technologies: A Roadmap for the Future
Amit Kumar Tyagi, Anand Kumar Mishra, Aswathy S. U. and Shabnam Kumari
18.1 Introduction to Quantum Computing and Its Related Terms
18.1.1 Quantum Computing
18.1.2 Qubits
18.1.3 Quantum Computation
18.1.4 Quantum Interference
18.1.5 Quantum Information
18.1.6 Quantum Superposition
18.1.7 Quantum Mechanics
18.1.8 Quantum Machine Learning
18.1.9 Quantum Deep Learning
18.1.10 Importance of Quantum Computer in Today’s Era
18.1.11 Organization of this Work
18.2 How Quantum Computing is Different from Security?
18.2.1 Quantum Computing vs Qubit vs Cryptography
18.3 Artificial Intelligence—Blockchain-Based Quantum Computing?
18.3.1 How Artificial Intelligence is Related to Quantum Computing?
18.3.2 How Blockchain is Related to Quantum Computing?
18.3.3 Artificial Intelligence-Based Quantum Computing
18.3.4 Artificial Intelligence—Blockchain-Based Quantum Computing
18.4 Process to Build a Quantum Computer
18.5 Popular Issues with Quantum Computing in this Smart Era
18.6 Problems Faced with Artificial Intelligence–Blockchain-Based Quantum Computing
18.7 Challenges with the Implementation of Quantum Computers in Today’s Smart Era
18.8 Future Research Opportunities with Quantum Computing
18.9 Future Opportunities with Artificial Intelligence–Blockchain-Based Quantum Computing
18.10 Conclusion
References
19. Qubits, Quantum Bits, and Quantum Computing: The Future of Computer
Security System
Harini S., Dharshini R., Praveen R., Abirami A., Lakshmanaprakash S. and Amit Kumar Tyagi
19.1 Introduction
19.2 Importance of Quantum Computing
19.3 Literature Survey
19.4 Quantum Computing Features
19.5 Quantum Algorithms
19.6 Experimental Results
19.7 Conclusion
References
20. Future Technologies for Industry 5.0 and Society 5.0
Mani Deepak Choudhry, S. Jeevanandham, M. Sundarrajan, Akshya Jothi, K. Prashanthini and V. Saravanan
20.1 Introduction
20.2 Related Work
20.3 Comparative Analysis of I4.0 to I5.0 and S4.0 to S5.0
20.4 Risks and Prospects
20.5 Conclusion
Acknowledgment
References
21. Futuristic Technologies for Smart Manufacturing: Research Statement and Vision for the Future
Amit Kumar Tyagi, Anand Kumar Mishra, Nalla Vedavathi, Vijayalakshmi Kakulapati and Sajidha S. A.
21.1 Introduction About Futuristic Technologies
21.2 Related Work Towards Futuristic Technologies
21.3 Related Work Towards Smart Manufacturing
21.4 Literature Review Towards Futuristic Technology
21.5 Motivation
21.6 Smart Applications
21.7 Popular Issues with Futuristic Technologies for Emerging Applications
21.7.1 Popular Issues with Futuristic Technologies for Smart Applications
21.7.2 Popular Issues with Futuristic Technologies for Smart Manufacturing
21.8 Legal Issues Towards Futuristic Technologies
21.9 Critical Challenges with Futuristic Technology for Emerging Applications
21.9.1 Critical Challenges with Futuristic Technology for Smart Applications
21.9.2 Challenges with Futuristic Technologies for Smart Manufacturing
21.10 Research Opportunities for Futuristic Technologies Towards Emerging Applications
21.10.1 Research Statements Towards Futuristic Technologies for Smart Applications
21.10.2 Research Opportunities for Futuristic Technologies Towards Smart Manufacturing
21.11 Lesson Learned
21.12 Conclusion
References
Index
Back to Top
Preface
Acknowledgements
Part 1: Fundamentals
1. Digital Twin Technology: Necessity of the Future in Education and Beyond
Robertas Damaševičius and Ligita Zailskaitė-Jakštė
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
2. An Intersection Between Machine Learning, Security, and Privacy
Hareharan P.K., Kanishka J. and Subaasri D.
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
3. Decentralized, Distributed Computing for Internet of Things-Based Cloud
Applications
Roopa Devi E.M., Shanthakumari R., Rajadevi R., Kayethri D. and Aparna V.
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
4. Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications for Next-Generation Society
V. Hemamalini, Anand Kumar Mishra, Amit Kumar Tyagi and Vijayalakshmi Kakulapati
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
5. Artificial Intelligence for Cyber Security: Current Trends and Future Challenges
Meghna Manoj Nair, Atharva Deshmukh and Amit Kumar Tyagi
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
6. An Automatic Artificial Intelligence System for Malware Detection
Ahmad Moawad, Ahmed Ismail Ebada, A.A. El-Harby and Aya M. Al-Zoghby
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
7. Early Detection of Darknet Traffic in Internet of Things Applications
Ambika N.
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
8. A Novel and Efficient Approach to Detect Vehicle Insurance Claim Fraud Using Machine Learning Techniques
Anand Kumar Mishra, V. Hemamalini, Amit Kumar Tyagi, Piyali Saha and Abirami A.
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
9. Automated Secure Computing for Fraud Detection in Financial Transactions
Kuldeep Singh, Prasanna Kolar, Rebecca Abraham, Vedantam Seetharam, Sireesha Nanduri and Divyesh Kumar
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
10. Data Anonymization on Biometric Security Using Iris Recognition Technology
Aparna D. K., Malarkodi M., Lakshmanaprakash S., Priya R. L. and Ajay Nair
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
11. Analysis of Data Anonymization Techniques in Biometric Authentication System
Harini S., Dharshini R., Agalya N., Priya R. L. and Ajay Nair
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
12. Detection of Bank Fraud Using Machine Learning Techniques
Kalyani G., Anand Kumar Mishra, Diya Harish, Amit Kumar Tyagi,bSajidha S. A. and Shashank Pandey
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
13. An Internet of Things-Integrated Home Automation with Smart Security System
Md. Sayeduzzaman, Touhidul Hasan, Adel A. Nasser and Akashdeep Negi
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
14. An Automated Home Security System Using Secure Message Queue
Telemetry Transport Protocol
P. Rukmani, S. Graceline Jasmine, M. Vergin Raja Sarobin, L. Jani Anbarasi and Soumitro Datta
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
15. Machine Learning-Based Solutions for Internet of Things-Based Applications
Varsha Bhatia and Bhavesh Bhatia
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
15.2.5 IoT Connectivity
15.2.6 Analytics and Data Management
15.2.7 Security and Privacy
15.2.8 Infrastructure
15.3 Importance of Data in IoT Applications
15.3.1 Data Gathered from IoT Applications
15.3.2 Quality of an IoT Application
15.3.3 Effective IoT Data Utilization
15.4 Machine Learning
15.4.1 Supervised Learning
15.4.2 Unsupervised Learning
15.4.3 Reinforcement Learning
15.5 Machine Learning Algorithms
15.5.1 k-Nearest Neighbors
15.5.2 Logistic Regression
15.5.3 Decision Tree
15.5.4 Random Forest
15.5.5 Support Vector Machines
15.5.6 Artificial Neural Networks
15.5.7 Long Short-Term Memory
15.6 Applications of Machine Learning in IoT
15.6.1 Smart City
15.6.2 Smart Agriculture
15.6.3 Smart Transportation
15.6.4 Smart Grid
15.6.5 Application in Supply Chain Management
15.6.6 Application in Wearable
15.6.7 Applications in Smart Factories
15.7 Challenges of Implementing ML for IoT Solutions
15.7.1 Privacy and Security
15.7.2 Scalability
15.7.3 Lack of Data
15.7.4 Data Quality
15.7.5 Interpretability
15.8 Emerging Trends in IoT
15.8.1 Edge Computing
15.8.2 5G and IoT
15.8.3 Artificial Intelligence and Machine Learning
15.8.4 Security
15.8.5 Blockchain
15.8.6 IoT and Cloud Computing
15.9 Conclusion
References
16. Machine Learning-Based Intelligent Power Systems
Kusumika Krori Dutta, S. Poornima, R. Subha, Lipika Deka and Archit Kamath
16.1 Introduction
16.2 Machine Learning Techniques
16.2.1 Classification Algorithm
16.2.1.1 K-Nearest Neighbor
16.2.1.2 Support Vector Machines
16.2.1.3 Decision Tree
16.2.1.4 Ensemble Boosted Trees
16.2.1.5 Random Forest
16.2.1.6 Naïve Bayes
16.2.1.7 Logistic Regression
16.2.2 Regression Analysis
16.2.2.1 Linear Regression
16.2.2.2 Regression Tree Ensemble
16.2.2.3 Elastic Net Regression
16.2.2.4 Gaussian Process Regression
16.2.2.5 Artificial Neural Networks
16.2.3 Deep Learning Techniques
16.2.3.1 Convolutional Neural Networks
16.2.3.2 Recurrent Neural Networks
16.2.4 Reinforcement Learning
16.3 Implementation of ML Techniques in Smart Power Systems
16.3.1 Fault Detection and Diagnosis
16.3.2 Load Forecasting
16.3.3 Load Disaggregation
16.3.4 Scheduling of Load
16.3.5 Energy Management
16.3.6 Asset Monitoring
16.4 Case Study
16.5 Conclusion
Further Reading
References
Part 4: Future Research Opportunities
17. Quantum Computation, Quantum Information, and Quantum Key Distribution
Mohanaprabhu D., Monish Kanna S. P., Jayasuriya J., Lakshmanaprakash S., Abirami A. and Amit Kumar Tyagi
17.1 Introduction
17.2 Literature Work
17.3 Motivation Behind this Study
17.4 Existing Players in the Market
17.5 Quantum Key Distribution
17.6 Proposed Models for Quantum Computing
17.7 Simulation/Result
17.7.1 Issues and Challenges in Quantum Computing
17.7.2 Issues in Quantum Key Distribution
17.7.3 The Future Ahead With Quantum Computation, Quantum Information, and Quantum Key Distribution
17.8 Conclusion
References
18. Quantum Computing, Qubits with Artificial Intelligence, and Blockchain Technologies: A Roadmap for the Future
Amit Kumar Tyagi, Anand Kumar Mishra, Aswathy S. U. and Shabnam Kumari
18.1 Introduction to Quantum Computing and Its Related Terms
18.1.1 Quantum Computing
18.1.2 Qubits
18.1.3 Quantum Computation
18.1.4 Quantum Interference
18.1.5 Quantum Information
18.1.6 Quantum Superposition
18.1.7 Quantum Mechanics
18.1.8 Quantum Machine Learning
18.1.9 Quantum Deep Learning
18.1.10 Importance of Quantum Computer in Today’s Era
18.1.11 Organization of this Work
18.2 How Quantum Computing is Different from Security?
18.2.1 Quantum Computing vs Qubit vs Cryptography
18.3 Artificial Intelligence—Blockchain-Based Quantum Computing?
18.3.1 How Artificial Intelligence is Related to Quantum Computing?
18.3.2 How Blockchain is Related to Quantum Computing?
18.3.3 Artificial Intelligence-Based Quantum Computing
18.3.4 Artificial Intelligence—Blockchain-Based Quantum Computing
18.4 Process to Build a Quantum Computer
18.5 Popular Issues with Quantum Computing in this Smart Era
18.6 Problems Faced with Artificial Intelligence–Blockchain-Based Quantum Computing
18.7 Challenges with the Implementation of Quantum Computers in Today’s Smart Era
18.8 Future Research Opportunities with Quantum Computing
18.9 Future Opportunities with Artificial Intelligence–Blockchain-Based Quantum Computing
18.10 Conclusion
References
19. Qubits, Quantum Bits, and Quantum Computing: The Future of Computer
Security System
Harini S., Dharshini R., Praveen R., Abirami A., Lakshmanaprakash S. and Amit Kumar Tyagi
19.1 Introduction
19.2 Importance of Quantum Computing
19.3 Literature Survey
19.4 Quantum Computing Features
19.5 Quantum Algorithms
19.6 Experimental Results
19.7 Conclusion
References
20. Future Technologies for Industry 5.0 and Society 5.0
Mani Deepak Choudhry, S. Jeevanandham, M. Sundarrajan, Akshya Jothi, K. Prashanthini and V. Saravanan
20.1 Introduction
20.2 Related Work
20.3 Comparative Analysis of I4.0 to I5.0 and S4.0 to S5.0
20.4 Risks and Prospects
20.5 Conclusion
Acknowledgment
References
21. Futuristic Technologies for Smart Manufacturing: Research Statement and Vision for the Future
Amit Kumar Tyagi, Anand Kumar Mishra, Nalla Vedavathi, Vijayalakshmi Kakulapati and Sajidha S. A.
21.1 Introduction About Futuristic Technologies
21.2 Related Work Towards Futuristic Technologies
21.3 Related Work Towards Smart Manufacturing
21.4 Literature Review Towards Futuristic Technology
21.5 Motivation
21.6 Smart Applications
21.7 Popular Issues with Futuristic Technologies for Emerging Applications
21.7.1 Popular Issues with Futuristic Technologies for Smart Applications
21.7.2 Popular Issues with Futuristic Technologies for Smart Manufacturing
21.8 Legal Issues Towards Futuristic Technologies
21.9 Critical Challenges with Futuristic Technology for Emerging Applications
21.9.1 Critical Challenges with Futuristic Technology for Smart Applications
21.9.2 Challenges with Futuristic Technologies for Smart Manufacturing
21.10 Research Opportunities for Futuristic Technologies Towards Emerging Applications
21.10.1 Research Statements Towards Futuristic Technologies for Smart Applications
21.10.2 Research Opportunities for Futuristic Technologies Towards Smart Manufacturing
21.11 Lesson Learned
21.12 Conclusion
References
Index
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