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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Acknowledgements
Part 1: Fundamentals
1 Digital Twin Technology: Necessity of the Future in Education and Beyond
1.1 Introduction
1.2 Digital Twins in Education
1.3 Examples and Case Studies
1.4 Discussion
1.5 Challenges and Limitations
1.6 Conclusion
References
2 An Intersection Between Machine Learning, Security, and Privacy
2.1 Introduction
2.2 Machine Learning
2.3 Threat Model
2.4 Training in a Differential Environment
2.5 Inferring in Adversarial Attack
2.6 Machine Learning Methods That Are Sustainable, Private, and Accountable
2.7 Conclusion
References
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
3.4 Implementation of Volunteer Edge Control
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
3.8 Serverless Cloud Platform System Design
3.9 Evaluation of HCloud
3.10 HCloud-Related Works
3.11 Conclusion
References
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
5 Artificial Intelligence for Cyber Security: Current Trends and Future Challenges
5.1 Introduction: Security and Its Types
5.2 Network and Information Security for Industry 4.0 and 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
5.12 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
5.16 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
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
7.1 Introduction
7.2 Literature Survey
7.3 Proposed Work
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
8.1 Introduction
8.2 Literature Survey
8.3 Implementation and Analysis
8.4 Conclusion
References
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
9.4 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
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
11.1 Introduction
11.2 Literature Survey
11.3 Existing Survey
11.4 Proposed System
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
12.1 Introduction
12.2 Literature Review
12.3 Problem Description
12.4 Implementation and Analysis
12.5 Results
12.6 Conclusion
12.7 Future Works
References
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
14 An Automated Home Security System Using Secure Message Queue Telemetry Transport Protocol
14.1 Introduction
14.2 Related Works
14.3 Proposed Solution
14.4 Implementation
14.5 Results
14.6 Conclusion and Future Work
References
15 Machine Learning-Based Solutions for Internet of Things-Based Applications
15.1 Introduction
15.2 IoT Ecosystem
15.3 Importance of Data in IoT Applications
15.4 Machine Learning
15.5 Machine Learning Algorithms
15.6 Applications of Machine Learning in IoT
15.7 Challenges of Implementing ML for IoT Solutions
15.8 Emerging Trends in IoT
15.9 Conclusion
References
16 Machine Learning-Based Intelligent Power Systems
16.1 Introduction
16.2 Machine Learning Techniques
16.3 Implementation of ML Techniques in Smart Power Systems
16.4 Case Study
16.5 Conclusion
Further Reading
References
Part 4: Future Research Opportunities
17 Quantum Computation, Quantum Information, and Quantum Key Distribution
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
17.8 Conclusion
References
18 Quantum Computing, Qubits with Artificial Intelligence, and Blockchain Technologies: A Roadmap for the Future
18.1 Introduction to Quantum Computing and Its Related Terms
18.2 How Quantum Computing is Different from Security?
18.3 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
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
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
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
21.8 Legal Issues Towards Futuristic Technologies
21.9 Critical Challenges with Futuristic Technology for Emerging Applications
21.10 Research Opportunities for Futuristic Technologies Towards Emerging Applications
21.11 Lesson Learned
21.12 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 5
Table 5.1 Analysis of several use cases of IoT with AI tools.
Chapter 6
Table 6.1 Summary of malware types.
Table 6.2 PE format description.
Table 6.3 APK format description.
Table 6.4 Summary of the malware analysis.
Table 6.5 Summary of the malware detection.
Table 6.6 An example of inserting a series of NOP.
Table 6.7 An example of unreachable instructions.
Table 6.8 An example of instruction reordering.
Table 6.9 An example of register reassignment.
Table 6.10 Summary of feature representation, extraction, and classification....
Table 6.11 Summary of AI studies for malware detection.
Chapter 9
Table 9.1 Keyword search in Web of Science and Google Scholar.
Chapter 11
Table 11.1 Advantages and disadvantages of the previously existing systems.
Table 11.2 Selected 25 elements from the unique vein patterns.
Table 11.3 Set of a generated master key (hexadecimal numbers).
Table 11.4 Generated master key after generalization.
Table 11.5 Master key after suppression.
Table 11.6 Master key after swapping.
Table 11.7 Master key after masking.
Table 11.8 FINAL key generated by the anonymization technique.
Chapter 12
Table 12.1 Classification report for k-nearest neighbors.
Table 12.3 Classification report for XGBoost.
Chapter 13
Table 13.1 LDR light intensity data.
Table 13.2 Supply voltage of the relay module.
Table 13.3 Electronic door lock operational data.
Table 13.4 Fire sensor operational data.
Table 13.5 Smoke sensor data.
Table 13.6 Google Assistant voice command compatibility.
Chapter 14
Table 14.1 Comparison of the different MCUs that can be used for implementatio...
Chapter 15
Table 15.1 Machine learning algorithms in Internet of Things.
Chapter 16
Table 16.1 ML algorithm for different applications.
Chapter 20
Table 20.1 I4.0 vs I5.0.
Table 20.2 Focus of S5.0.
Table 20.3 Skillsets required for I5.0 and S5.0.
Table 20.4 Challenges and opportunities of I5.0 and S5.0.
List of Illustrations
Chapter 2
Figure 2.1 Training and inference phase of a model.
Chapter 3
Figure 3.1 Use of blockchain technology in cloud computing.
Figure 3.2 Elements of each component of KubeEdge.
Figure 3.3 Composition of robot formation system.
Figure 3.4 The volunteer edge cloud’s workflow.
Figure 3.5 (i) The outcome obtained during the execution of the program. (ii) ...
Figure 3.6 HCloud virtual view.
Figure 3.7 Architecture of cloud manager.
Figure 3.8 Policies for scheduling.
Figure 3.9 Throughput of different clouds and HCloud.
Chapter 5
Figure 5.1 Explanation of this work.
Figure 5.2 Various elements and components of Society 5.0.
Figure 5.3 Evolution of Society 5.0 and Industry 4.0.
Figure 5.4 Progression of cyber and physical threats for each industrial revol...
Figure 5.5 Traditional security vs cloud-based security.
Chapter 6
Figure 6.1 Multi-stage cybercrime attacks.
Figure 6.2 Windows PE format.
Figure 6.3 Linux ELF format.
Figure 6.4 Android APK format.
Figure 6.5 Reverse process using IDA disassembler.
Figure 6.6 Dynamic analysis of malware inside a controlled environment.
Figure 6.7 Malware analysis hierarchy.
Figure 6.8 Malware detection hierarchy.
Figure 6.9 Difference between AI, ML, and DL.
Figure 6.10 Different features of extraction form static or dynamic malware.
Figure 6.11 Feature representation.
Figure 6.12 Malware binary image.
Figure 6.13 General architecture of AI malware detection.
Chapter 7
Figure 7.1 The association between cyberspace, deep net, and dark web [4].
Figure 7.2 Darknet-IDS of IoT (DTDS-IoT) network traffic via ML techniques [13...
Figure 7.3 Proposed work.
Figure 7.4 Comparison of the speed of two suggestions.
Chapter 8
Figure 8.1 Dataset.
Figure 8.2 Methodology.
Figure 8.3 K-NN.
Figure 8.4 Decision tree classifier.
Figure 8.5 Random forest classifier.
Figure 8.6 Checking for missing values.
Figure 8.7 Information about the dataset.
Figure 8.8 Fraud type.
Figure 8.9 Count for each sex attribute.
Figure 8.10 Accident area.
Figure 8.11 Counts of vehicle according to age.
Figure 8.12 Heat map of the correlation matrix.
Figure 8.13 Fault.
Figure 8.14 Fraud frequency for gender.
Figure 8.15 Data encodings.
Figure 8.16 Feature selection using RFE.
Figure 8.17 Head of the new dataset.
Figure 8.18 New data correlation matrix.
Figure 8.19 Model building using KNN.
Figure 8.20 Model building using decision tree.
Figure 8.21 Model building using random forest.
Figure 8.22 Undersampling.
Figure 8.23 Oversampling.
Figure 8.24 Comparison of f1_score.
Figure 8.25 Bar chart comparing the f1_score.
Chapter 9
Figure 9.1 Types of financial frauds.
Figure 9.2 Types of bank frauds.
Figure 9.3 Co-occurrence of keywords
Figure 9.4 Proposed model.
Chapter 10
Figure 10.1 Different steps in iris recognition.
Chapter 11
Figure 11.1 Process involved.
Figure 11.2 Features on a palm vein [13].
Figure 11.3 Palmprint features [13].
Figure 11.4 Result of using a Canny detector [13].
Figure 11.5 Difference between a retina (left) and an iris (right).
Figure 11.6 Biometric system architecture.
Figure 11.7 Extraction of unique traits, noise removal, and capturing the main...
Chapter 12
Figure 12.1 Workflow of the project.
Figure 12.2 Banksim dataset.
Figure 12.3 Head of dataset.
Figure 12.4 Data frames created.
Figure 12.5 Fraudulent data in the dataset.
Figure 12.6 Boxplot.
Figure 12.7 Heat map of the correlation matrix.
Figure 12.8 Histogram of fraudulent and non-fraudulent payments.
Figure 12.9 Data preprocessing.
Figure 12.10 Data transformation.
Figure 12.11 Dependent and independent variables.
Figure 12.12 Function of the ROC AUC curve.
Figure 12.13 Classification report and ROC curve for KNN performance.
Figure 12.14 Classification report and ROC curve for random forest classifier....
Figure 12.15 Classification report and ROC curve for XGBoost.
Figure 12.16 Classification report and ROC curve for decision tree classifier....
Figure 12.17 Benford’s Law and empirical distribution.
Chapter 13
Figure 13.1 Block diagram of an a smart IoT-based smart home with a security s...
Figure 13.2 Remote-controlled operation.
Figure 13.3 Voice-controlled operation.
Figure 13.4 ESP32 cam [18].
Figure 13.5 PIR sensor [19].
Figure 13.6 Relay module [20].
Figure 13.7 Keypad [21].
Figure 13.8 LCD display [22].
Figure 13.9 Electric door lock [23].
Figure 13.10 DHT11 sensor [24].
Figure 13.11 GSM module [25].
Figure 13.12 Smoke and gas sensor [26].
Figure 13.13 Flame sensor [27].
Figure 13.14 Arduino UNO [28].
Figure 13.15 ESP8266 [29].
Figure 13.16 Resistor [30].
Figure 13.17 LED light [31].
Figure 13.18 Holder [32].
Figure 13.19 Jumper and electric wire [33].
Figure 13.20 Implemented hardware of the prototype.
Figure 13.21 IFTTT implementation, part 1.
Figure 13.22 IFTTT implementation, part 2.
Figure 13.23 Adafruit interface of the prototype.
Figure 13.24 Graphical form of the light intensity worktime.
Figure 13.25 Google Assistant with the aid of IFTTT.
Chapter 14
Figure 14.1 Nature-Wise property stolen in completed robberies in 2020 [4].
Figure 14.2 Encryption time vs. file size for DES, 3DES, AES, Blowfish, and RS...
Figure 14.3 NodeMCU (top view).
Figure 14.4 NodeMCU (PIN view).
Figure 14.5 PIR sensor PIN view (left); PIR sensor top view (right).
Figure 14.6 Proposed system architecture.
Figure 14.7 Workflow of the UI and control system.
Figure 14.8 Workflow of the hardware module.
Figure 14.9 AES security workflow.
Figure 14.10 Facial recognition workflow.
Figure 14.11 Node-Red flow for the controller module.
Figure 14.12 Facial recognition working on top of the video feed.
Figure 14.13 The user is notified via email about the unknown entries into the...
Figure 14.14 Working UI showing the hardware’s detection of motion.
Figure 14.15 Presentation of the functioning of the security module.
Chapter 15
Figure 15.1 Implementing machine learning in IoT-based solutions.
Figure 15.2 Machine learning for IoT applications.
Chapter 16
Figure 16.1 Intelligent power systems.
Figure 16.2 K-nearest neighbor algorithm.
Figure 16.3 Support vector machine.
Figure 16.4 Linear SVM.
Figure 16.5 Kernel SVM.
Figure 16.6 Structure of DT.
Figure 16.7 Sigmoid function used in LR.
Figure 16.8 Linear regression model.
Figure 16.9 Structure of the feed forward neural network.
Figure 16.10 Feature map and convolution filter/kernel.
Figure 16.11 Activation function.
Figure 16.12 Max pooling.
Figure 16.13 Pooling and flattening.
Figure 16.14 (a) Architecture of single neuron, simple RNN and GRU. (b) Archit...
Figure 16.15 Reinforcement learning process in a game theory.
Figure 16.16 Optimal scheduling of load.
Figure 16.17 Processes involved in energy management.
Figure 16.18 Simulink model of the IEEE five-bus system.
Figure 16.19 Accuracy performance of the various ML models.
Chapter 19
Figure 19.1 Bit vs. qubit.
Figure 19.2 Superposition.
Figure 19.3 Entanglement.
Figure 19.4 Parallelism.
Figure 19.5 Interference.
Figure 19.6 Error correction.
Figure 19.7 PTM’s state transition diagram.
Figure 19.8 A PTM computation tree.
Figure 19.9 QTM begins in state 0.
Figure 19.10 QTM begins in state 1.
Chapter 20
Figure 20.1 Evolution from I1.0 to I5.0.
Figure 20.2 Evolution of S1.0 to S5.0.
Figure 20.3 Transformation of the traditional domain to the I4.0 and S4.0 doma...
Figure 20.4 KET for I4.0 to I5.0.
Guide
Cover
Table of Contents
Series Page
Title Page
Copyright
Preface
Acknowledgements
Begin Reading
Index
End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Automated Secure Computing for Next-Generation Systems
Edited by
Amit Kumar Tyagi
National Institute of Fashion Technology, New Delhi, India
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-213597
Cover images: Russell RichardsonCover design by Pixabay.Com
Preface
Security is always a primary concern to each application and sector. In the last decade, many techniques and frameworks have been suggested by several researchers to improve security (data, information, and network). Due to rapid improvement in industry automation, however, systems need to be secured more quickly and efficiently. Artificial Intelligence (AI) and Machine Learning (ML) have been put thoroughly into practice to enhance continuity, cybersecurity, and security in cloud computing, internet services, and the Internet of Things and its related applications. Computer vision based algorithms, such as ML and AI, are used to track complex cyber threats that cannot be readily identified by conventional detection methods. 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 and undisclosed work 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, the book 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 about the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. In summary, this book includes all possible topics based on machine empowered solutions for next generation secure systems.
Amit Kumar Tyagi
October 2023
Acknowledgements
First, we extend our gratitude to our family members, friends, and supervisors who stood by us as advisors during the completion of this book. Also, we thank our almighty God who inspired us to write this book. Furthermore, we thank Wiley and Scrivener Publishing, who have provided continuous support; and our colleagues with whom we have worked inside the college and university system, as well as those outside of academia who have provided their endless support toward completing this book.
Finally, we wish to thank our Respected Madam, Prof. G Aghila, Prof. Siva Sathya, our Respected Sir Prof. N Sreenath, and Prof. Aswani Kumar Cherukuri, for their valuable input and help in completing this book.
Amit Kumar Tyagi
October 2023
Part 1FUNDAMENTALS
1Digital Twin Technology: Necessity of the Future in Education and Beyond
Robertas Damaševičius1*and Ligita Zailskaitė-Jakštė2
1Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
2Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
Abstract
This chapter provides an overview of digital twin technology (DTT) and its applications in various industries including education. DTT involves the creation of exact digital replicas of physical entities that can reflect real-time changes in the underlying entity. The chapter explores the potential of DTT for enhancing the learning experience and improving the educational outcomes through immersive hands-on learning, replication of real-world scenarios for scientific inquiry and problem-solving, and personalization of the learning process. The chapter also examines the challenges of implementing DTT in education, including technical, pedagogical, ethical, and privacy concerns. It also discusses the potential of DTT for shaping the future of education and beyond as well as future research directions.
Keywords: Digital twin technology, education, virtual reality, avatar, immersive learning, personalized learning, real-world scenarios
1.1 Introduction
Digital twin technology (DTT) allows the creation of an exact digital replica of a physical entity and continuously feeds it with real-time data from integrated sensors and other devices [6]. The foundation of DTT is the ability to simulate the behavior of a physical entity in a virtual environment [62]. By gathering data from sensors and other devices, the digital twin is able to mirror the actions, interactions, and changes of the underlying physical entity in cyberspace [61]. This real-time data flow creates a bridge between the physical and digital worlds, allowing us to understand and predict the behavior of the physical entity in ways that were never before possible [72]. However, the true power of DTT lies in its ability to go beyond mere observation and prediction. With the use of advanced algorithms and simulations, the digital twin can be used to improve the performance of the physical entity and even to control it in real time. This opens up new possibilities for innovation and optimization in fields such as manufacturing, transportation, healthcare, etc. [10]. It can change the way we design, build, and operate the systems that shape our world, and it is a necessary component for the Industry 4.0 Revolution [35], the future of education, and beyond. DTT has a wide range of applications across various industries. Some of the most notable applications include the following:
Manufacturing: DTT can be used to optimize the performance of production lines and machines by simulating and predicting their behavior under different conditions. This can help to identify and resolve bottlenecks, reduce downtime, and improve overall efficiency [
18
].
Transportation: DTT can optimize the performance of transportation systems, such as trains, buses, and logistics networks [
52
]. By simulating and predicting the behavior of these systems, it is possible to identify and resolve issues, improve efficiency, and reduce costs.
Healthcare: DTT can be used to optimize the performance of medical equipment, such as MRI scanners and X-ray machines, and to simulate and predict the behavior of patients [
31
], which can reduce costs and improve patient outcomes.
Aerospace: DTT can be used in the aerospace industry to optimize the performance of aircraft and spacecraft by simulating and predicting their behavior under different conditions [
41
]. This can help to improve safety and increase efficiency.
Infrastructure design: DTT can be used to optimize the performance of buildings, bridges, and other structures by simulating and predicting their behavior under different conditions [
5
]. This can help to reduce costs and improve safety.
Smart cities: DTT can be used to optimize the performance of smart cities by simulating and predicting the behavior of infrastructure, transportation systems, and other key components of the city [
64
]. This can help to reduce costs, improve safety, and increase efficiency.
Agriculture: DTT can be used to improve the efficiency and productivity of agricultural operations by optimizing planting, irrigation, and fertilization schedules, monitoring the health and well-being of livestock, monitoring and optimizing the performance of agricultural equipment, creating accurate weather forecasts, creating predictive models of crop yields, and monitoring the farm remotely. This can help farmers in making more informed decisions and can improve the overall efficiency and productivity of their work [
46
].
Energy and utilities: DTT can be used to optimize the performance of energy and utility systems, such as power plants, water treatment facilities, and pipelines (see “Teaching Factory” [
44
]). By simulating and predicting their behavior under different conditions, one can identify and resolve issues, improve efficiency, and reduce costs.
Retail: DTT can be used to optimize the performance of retail operations, such as stores, warehouses, and logistics networks [
19
]. By simulating and predicting their behavior under different conditions, it is possible to identify and resolve issues, improve efficiency, and reduce costs.
These are just a few examples of the many different applications of DTT across various industries. As technology continues to advance and the data generated by digital twin systems becomes more accurate, the potential for DTT to improve business processes, reduce costs, and increase efficiency is expected to continue to grow [55].
The aim of this chapter is to examine the role and impact of DTT in shaping the future of education and beyond. It aims to provide an overview of DTT and its applications in various industries and delve into the specific ways in which DTT can be utilized in education. The chapter explores the potential of DTT to support the evolution of intelligence and personalization in education and discusses the challenges and limitations that must be considered in the implementation of DTT in education.
The novelty of this chapter is that it provides a comprehensive examination of DTT and its potential impact on education, which has not been fully explored before. This chapter brings together the latest research and developments in DTT and education to present a holistic view of the current state of the field. The chapter also discusses the challenges and limitations that must be considered in the implementation of DTT in education, which is an important aspect that is rarely highlighted in other studies.
The contribution of this chapter is that it highlights the potential of DTT to shape the future of education and beyond. It argues that DTT is a necessary component for enhancing the learning experience and improving outcomes and that its potential must be fully realized in order to achieve this. The chapter contributes to the understanding of the challenges and limitations that must be considered in the implementation of DTT in education. By highlighting these challenges, the chapter aims to help researchers and practitioners navigate them and ensures that DTT is implemented in the most effective way possible.
1.2 Digital Twins in Education
1.2.1 Virtual Reality for Immersive Learning
Virtual reality (VR) and computer vision (CV) are two technologies that can be used to enhance the learning experience through immersive hands-on learning when combined with DTT. VR is an artificial simulation of a 3D environment which a person can explore and interact with [51]. VR can be used to create immersive and interactive learning environments that allow students to experience and interact with virtual objects and scenarios as if they were real [36]. This allows students to have hands-on experience in simulations and helps them to better understand and retain the teaching material [66]. CV is a field of artificial intelligence (AI) that deals with the development of systems that can interpret and understand visual information [23]. CV can be used to enhance the realism of virtual environments and to track the movement of students in VR. This allows students to interact with virtual environments in a more natural and intuitive way and enhances the sense of immersion in the learning experience [65].
When combined with DTT, VR and CV can be used to create immersive learning environments that replicate real-world scenarios [58]. This allows students to practice and experience the material in a realistic way and helps them to better assimilate and retain the material—for example, in a medical education context, students can practice performing surgeries in a VR environment that replicates a real-world scenario, which can help them to better understand and retain the material and also to be better prepared for real-world scenarios. The use of VR and CV in combination with DTT also allows for the creation of personalized learning experiences—for example, using CV methods, the system can track and analyze the student’s movements and interactions in the virtual environment and provide feedback and guidance accordingly. This allows for more individualized instruction and support and can help students to learn at their own pace.
Another advantage of using VR and CV in combination with DTT is the ability to conduct experimentation and scientific inquiry in a safe and controlled environment—for example, in a physics class, students can conduct experiments in a virtual environment that replicates the real-world scenario and test different variables and conditions. This allows the students to explore and understand scientific concepts in a more interactive and engaging way and can help them to better understand and retain the material. In DTT, avatars [47] can be used to represent real-world objects and entities, such as vehicles, buildings, or even entire cities. An avatar refers to a digital representation of a person typically used in VR environments and simulations. They can also be used to represent virtual entities, such as robots, drones, or even abstract concepts. Avatars can be used to simulate human behavior and interactions in digital twin scenarios. They can also be used as a way for users to interact and communicate with the virtual environment and with other avatars. Avatars can be created in a variety of forms, such as 3D models, digital characters, or even simple icons. They can be customized to represent the user’s physical characteristics, such as height, weight, and facial features. They can also be programmed to exhibit different behaviors, movements, and expressions to simulate human-like interactions. Avatars can be controlled by the user, or they can be programmed to operate autonomously and exhibit behaviors based on rules and algorithms. In this way, the avatars can be used to simulate real-world scenarios and help users to understand complex systems or processes and to make predictions about the behavior of these systems.
DTT also provides a key component for the metaverse. The metaverse is a term used to describe a virtual universe that combines elements of the real world and the digital world [3]. It is a concept that represents an extension of the Internet where users can perform actions in virtual environments with digital objects and other users in a seamless and immersive way. DTT is a key enabler for the metaverse, as it allows for the creation of exact digital replicas of real-world objects, environments, and systems that can be used to connect the physical and virtual worlds and provide a more immersive and realistic experience for users in the metaverse.
1.2.2 Delivery of Remote Education
The importance of remote education has grown in recent years especially due to the COVID-19 pandemic [56]. DTT can be used to enable and support the delivery of remote education (distance/online teaching) in a number of ways, namely:
Virtual classrooms: DTT can be used to create virtual classrooms that mimic the physical classroom environment. It can provide students with a sense of presence and engagement and enable real-time interactions with the teacher and other students [
8
].
Real-time monitoring and feedback: DTT can be used to monitor student engagement, progress, and understanding and provide real-time feedback to the teacher and student [
54
]. This can help to ensure that the student is keeping pace with the class and addressing any gaps in understanding.
Collaborative learning: DTT can be used to create collaborative learning environments that allow students to work together on projects, assignments, and simulations [
60
]. This can help to promote teamwork and collaboration and can make the remote learning experience more enjoyable and interactive.
Remote access: DTT can provide remote access to educational resources, which can enable students to learn from anywhere at any time [
50
,
68
].
DTT can be used to support the delivery of remote education by providing students with a sense of presence and engagement, real-time monitoring and feedback, personalized learning, simulation-based learning, collaborative learning, and remote access to educational resources. This can help to improve the effectiveness and efficiency of distance/online teaching and can make the learning experience more interactive and engaging for students.
1.2.3 Replication of Real-World Scenarios
DTT is increasingly being used to replicate real-world scenarios for scientific inquiry and problem-solving. The ability to create an exact digital replica of a physical entity or process and feeding it with real-time data from sensors and other devices allow for a wide range of experimentation and exploration in a safe and controlled environment. One of the advantages of using DTT for scientific inquiry and problem-solving is the ability to replicate real-world scenarios in a virtual environment. This allows for the testing and exploration of different variables and conditions without the need for expensive and time-consuming physical experiments, e.g., in engineering, scientists can use DTT to simulate the behavior of a bridge under different loads and conditions, which would be expensive and dangerous to replicate in the real world. DTT can also be used to replicate real-world scenarios in healthcare—for example, DTT can be used to simulate the state of a patient’s body under different medical treatments, which would be impossible to replicate in the real world. This allows for the exploration of different treatment options and can help to identify an effective treatment for a particular patient. In education, DTT can be used to replicate real-world scenarios for scientific inquiry and problem-solving—for example, students can use DTT to explore and experiment with different ecological environments and the impact of biodiversity on the ecosystem without the need for expensive and time-consuming field trips. This allows for the development of scientific inquiry and problem-solving skills and can help students to better understand and retain the material.
1.2.4 Promote Intelligences and Personalization
The use of DTT in the education system can provide a comprehensive approach to the digitalization of education, setting new goals and changing the educational process [37]. By creating a digital twin of the learner, the system can be used to improve the digitalization of the learning process and the entire educational organization by automatically searching for suitable disciplines, learning technologies, and forming individual educational trajectories. The data collected can also be used for recruitment, career guidance, and management activities of the educational organization [1].
Specifically, DTT has the potential to promote multiple intelligences [32] and personalization in education through the use of immersive and interactive learning environments. By providing students with a virtual environment that replicates real-world scenarios, DTT can help to develop students’ spatial intelligence as they navigate and interact with the virtual environment. DTT can help to develop students’ logical–mathematical intelligence as they conduct experimentation and problem-solving in the virtual environment. DTT can also be used to promote the development of interpersonal and intrapersonal intelligences through the use of personal virtual tutors. Personal virtual tutors [34] can be used to provide individualized instruction and support and can help students develop their social and emotional intelligence, as they interact with the virtual tutor, and to provide feedback and guidance, which can help students develop their self-awareness and self-regulation. DTT also allows for personalization in education by allowing students to interact with learning resources that are tailored to their individual needs. Through the use of intelligent recommendations and learning analytics, DTT can help to identify and provide students with learning resources that are best suited to their individual needs. This can help to promote personalization in education and can lead to better learning outcomes.
Therefore, DTT has the potential to support personalization by providing students with immersive and interactive learning environments, personal virtual tutors, and personalized learning resources. DTT can help to enhance the learning experience and improve outcomes. DTT can also help to foster a more engaging and interactive learning experience, as it allows students to conduct experimentation, scientific inquiry, and problem-solving in a safe and controlled environment. This can help to develop critical thinking and problem-solving skills and can help students better understand and retain the material. Furthermore, it can provide an effective way for students to practice and experience the material in a realistic way, which can lead to better understanding, retention, and preparation for real-world scenarios.
1.3 Examples and Case Studies
1.3.1 Examples of DTT in Education
DTT is increasingly being used in education to enhance the learning experience and improve outcomes. Some examples of DTT in education include the following:
Virtual labs: DTT can be used to create virtual labs that replicate real-world scenarios, allowing students to conduct experiments and explore scientific concepts in a safe and controlled environment without the need for expensive and time-consuming field trips [
29
,
57
].
Language learning: DTT can be used to create immersive and interactive language learning environments that replicate real-world scenarios—for example, students can use DTT to explore and interact with virtual environments that simulate different cultures and languages, which can help to improve their understanding and retention of the material [
15
].
Science, Technology, and Mathematics (STEM) education: DTT can be used to create immersive and interactive STEM learning environments that replicate real-world scenarios—for example, students can use DTT to explore and interact with virtual environments that simulate different engineering or physics concepts [
16
] or control robots [
11
], which can improve their understanding and retention of the material.
Serious educational games: DTT can be used to create immersive and inter-active learning experiences that closely mimic real-world scenarios [
38
]—for example, a digital twin of a factory or power plant can be used in a game that teaches students about industrial processes and systems. The game can include challenges and puzzles that require players to apply their knowledge of the systems to solve problems [
40
], and the digital twin can be used to provide feedback and guidance on their performance. Additionally, digital twins can be used in games that teach subjects like physics, engineering [
13
], and architecture by allowing players to create and experiment with virtual models of processes, structures, and systems [
12
,
16
].
Architecture and design education: DTT can be used to create virtual models of buildings and other structures for architectural design, engineering, and construction education [
63
]. This allows students to explore and experiment with different design options in a safe and controlled environment, thus improving their understanding and retention of the material.
Virtual field trips: DTT can be used to create virtual field trips that replicate real-world scenarios, allowing students to explore different locations and historical sites without the need for expensive and time-consuming physical trips. This can help to expand students’ knowledge and understanding of different cultures and historical events.
Professional training: DTT can be used to create virtual training environments that replicate real-world scenarios, allowing students to practice in a realistic way [
42
]—for example, in fields such as aviation, engineering, and manufacturing, students can use DTT to practice operating equipment and machinery, which can help to prepare them for real-world scenarios and improve their understanding and retention of the material.
Space education: DTT can be used to create virtual environments that replicate space and planetary environments, allowing students to explore and understand scientific concepts related to space and astronomy. This can help to develop students’ spatial intelligence [
69
] as well as their understanding of scientific concepts related to space.
Art and design education: DTT can be used to replicate real-world art and design scenarios, allowing students to explore and experiment with different design options in a safe and controlled virtual environment [
59
]. This can help to improve their understanding and retention of the material and develop their creative talents.
These are some examples of how DTT is being used in education to enhance the learning experience and improve outcomes. As technology continues to advance, DTT is expected to play an increasingly important role in shaping the future of education.
1.3.2 Digital Twin-Based Educational Systems
There are several specific examples of digital twin-based educational systems that are currently being used or developed in various fields of education, namely:
Virtual anatomy lab [
4
] allows medical students to explore and study the human body in a virtual environment. The system uses 3D imaging and VR technology to create a digital replica of the human body which students can interact with and explore in a safe and controlled environment.
Virtual ship design platform [
48
] allows engineering students to design and test virtual ships in a virtual environment. The system uses DTT to create a replica of the ship which students can use to explore different design options and test the ship’s performance in a variety of conditions.
Virtual chemistry lab [
21
] allows students to conduct experiments and explore scientific concepts in a virtual environment. The system creates a replica of a chemistry lab which students can use to explore different chemical reactions and test different variables in a safe and controlled environment.
Virtual field trips can be used to take students to different historical sites or geographical locations without the need for physical field trips [
3
]. This can be done by creating virtual environments that replicate real-world scenarios which students can explore and interact with using VR technology.
Virtual nature is an example of a digital twin-based educational system that simulates natural environments and ecosystems [
26
]. It allows students to explore and interact with virtual representations of real-world natural environments, such as forests, oceans, and wetlands. The system can be used to teach subjects such as biology, ecology, and environmental science. The virtual nature system can be accessed through VR technology which provides an immersive and interactive experience for students. They can explore virtual environments and observe the behavior of virtual plants and animals in real time, thus simulating the natural world. This allows students to observe and study natural phenomena that may be difficult or impossible to observe in the real world, such as the behavior of rare or endangered species or the effects of climate change on ecosystems. The digital twin-based nature system also allows for experimentation and data collection—for example, students can conduct virtual experiments to study the impact of different variables on the ecosystem, such as the introduction of new species, changes in temperature, or pollution. This enables them to explore cause-and-effect relationships and develop scientific inquiry skills [
43
]. Moreover, virtual nature can also be used as an interactive and engaging way to teach conservation and sustainability, as learners can explore the impacts of human activities on the environment and the effects of different conservation strategies.
Virtual manufacturing system [
7
] allows students to practice and experience the material in a realistic way. This can be done by creating virtual environments that replicate real-world manufacturing scenarios which students can explore and interact with using VR technology.
Virtual power plant allows students to explore and understand the complexities of power generation and distribution. The students use a replica of a power plant to explore different power generation options, test different configurations, and understand the impact of various parameters on the power plant’s performance in a simulated environment [
22
].
Virtual construction site [
70
] allows students to learn about construction management and engineering by exploring and interacting with a virtual replica of a construction site. The system uses DTT to create a replica of a construction site which students can use to explore different construction options, test different configurations, and understand the impact of various parameters on the construction project’s performance.
Virtual aerospace [
45
] allows students to learn about aerospace engineering by exploring and interacting with virtual replicas of aircrafts and spaceships. The system uses DTT to create a replica of an aircraft which students can use to explore different design options, test different configurations, and understand the impact of various parameters on the aircraft’s performance.
Virtual automotive [
30
] allows students to learn about automotive engineering by exploring and interacting with virtual replicas of cars and trucks. The system uses DTT to create a replica of a car or truck which students can use to explore different design options, test different configurations, and understand the impact of various parameters on the car’s or truck’s performance.
Virtual disaster response allows students to learn about emergency management and response by exploring and interacting with virtual replicas of disaster scenarios. The system uses DTT to create a replica of a disaster scenario which students can use to explore different response options, test different configurations, and understand the impact of various parameters on the disaster response’s performance.
Virtual library is a virtual representation of the physical library space and its collections. This can include a 3D model of the library building as well as digital representations of the books, journals, and other materials in the library’s collection. This digital twin can be used in a variety of ways to enhance the user experience in a virtual library—for example, it can be used to provide virtual tours of the library space, allowing users to explore the library and its collections from the comfort of their own homes. It can also be used to create interactive exhibits, games, and educational resources that allow users to learn more about the library’s collections in an engaging and interactive way. Additionally, digital twin can be used to track and analyze the usage of the library, such as the number of visitors, the most popular books and resources, and the busiest hours of the day, which can be used for library planning and decision making [
28
].
Virtual museum is a virtual representation of the physical museum space and its collections. This can include a 3D model of the museum building and digital representations of the artifacts, artworks, and other objects in the museum’s collection. This digital twin can be used in a variety of ways to enhance the user experience in a virtual museum—for example, it can be used to provide virtual tours of the museum space, allowing users to explore the museum and its collections from the comfort of their own homes. It can also be used to create interactive exhibits, games, and educational resources that allow users to learn more about the museum’s collections in an engaging and interactive way [
33
].
These are some examples of digital twin-based educational systems that are currently being used or developed in different fields of education. They are helping to enhance the learning experience and improve outcomes by providing students with immersive and interactive learning environments and allowing them to conduct experimentation and problem-solving in a safe and controlled environment [17].
1.4 Discussion
We evaluate the effectiveness of DTT for education in terms of five main outcomes of learning (intellectual skills, cognitive strategy, verbal information, motor skills, and attitude) [24] as follows:
Intellectual skills: DTT can be used to create virtual simulations of real-world scenarios that can be used to teach a wide range of concepts, rules, and procedures—for example, a digital twin of a manufacturing facility can be used to teach students about industrial processes, or a digital twin of a city can be used to teach students about urban planning. These simulations can provide a more interactive and engaging learning experience, allowing students to apply their knowledge in a realistic setting.
Cognitive strategy: DTT can be used to create personalized learning experiences that adapt to the individual needs and learning style of each student—for example, DTT can be used to create virtual tutors that can interact with students and provide feedback and guidance based on their performance. This can help students to develop their own cognitive strategies for learning and problem-solving.
Verbal information: DTT can be used to create virtual environments that allow students to interact with and explore a wide range of information—for example, a digital twin of a historical site can be used to provide students with information about the history and culture of the site. This can help students to better understand and retain verbal information by providing a more interactive and engaging learning experience.
Motor skills: DTT can be used to create virtual simulations of real-world tasks that can be used to teach students a wide range of motor skills—for example, a digital twin of a surgical procedure can be used to teach students about surgical techniques, or a digital twin of a sports training facility can be used to teach students about physical conditioning. These simulations can provide students with a safe and controlled environment in which to practice and develop their motor skills.
Attitude: DTT can be used to create virtual environments for teaching students about different attitudes and perspectives—for example, a digital twin of a city can be used to teach students about different cultures and customs, or a digital twin of an ecosystem can be used to teach students about conservation and sustainability. These simulations can provide students with a deeper comprehension of the subject matter and can help to develop positive attitudes and behaviors.
As a result, DTT can be used to support all five types of learning outcomes by providing students with interactive and engaging learning experiences that allow them to explore, practice, and apply their knowledge in realistic settings. By using DTT, educators can create personalized and adaptive learning experiences that support the development of intellectual skills, cognitive strategies, verbal information, motor skills, and attitudes. Additionally, the use of DTT can also support the development of other important skills such as collaboration, critical thinking, and problem-solving, which are essential for success in today’s digital economy.
Other criteria are based on the levels of Revised Blooms’ Taxonomy (RBT) [9]. DTT can support learning objectives across all levels of RBT, namely:
Remembering: DTT can create interactive and engaging learning experiences that help learners remember and recall information—for example, learners can use DTT to explore virtual environments which can help them to remember and recall information about a specific topic or subject.
Understanding: DTT can help learners understand complex concepts and ideas—for example, learners can use DTT to interact with virtual models of physical systems which can help them understand how the systems work and how different variables affect the system’s performance.
Applying: DTT can help learners adopt their skills and knowledge to real-life problems—for example, learners can use DTT to practice and apply their knowledge in virtual simulations of real-world scenarios.
Analyzing: DTT can support the analysis of information and data—for example, learners can use DTT to analyze data from virtual experiments or simulations and make predictions based on the data.
Evaluating: DTT can support the evaluation of information and data—for example, learners can use DTT to evaluate the results of virtual experiments or simulations and make judgments about the quality of the data.
1.5 Challenges and Limitations
1.5.1 Technical Challenges
There are several technical challenges that need to be considered when implementing DTT in education. Some of these challenges include the following:
Data management: One of the main challenges of implementing DTT in education is the management of large amounts of data. Digital twins require real-time data updates to be accurate, and this data needs to be stored and managed in an efficient way. This can be a complex and time-consuming task, particularly in educational environments where large amounts of data are generated [
2
].
Integration with existing systems: Another challenge is integrating DTT with existing systems and infrastructure. Educational institutions often have existing systems and technologies in place that need to be integrated with DTT to provide a seamless and effective learning experience.
Hardware and software requirements: DTT requires a significant investment in hardware and software, including high-performance computers, sensors, and VR equipment. This can be a significant challenge for educational institutions, particularly those with limited budgets.
Scalability: DTT can be complex and resource-intensive, making it difficult to scale up to meet the needs of large numbers of students or multiple educational institutions. This can be a significant challenge when implementing DTT in education.
