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.
Preface xvii Acknowledgements xix Part 1: Fundamentals 1 1 Digital Twin Technology: Necessity of the Future in Education and Beyond 3 Robertas Damasevicius and Ligita Zailskaite-Jakste 1.1 Introduction 3 1.2 Digital Twins in Education 5 1.3 Examples and Case Studies 8 1.4 Discussion 12 1.5 Challenges and Limitations 13 1.6 Conclusion 17 2 An Intersection Between Machine Learning, Security, and Privacy 23 Hareharan P.K., Kanishka J. and Subaasri D. 2.1 Introduction 23 2.2 Machine Learning 24 2.3 Threat Model 27 2.4 Training in a Differential Environment 30 2.5 Inferring in Adversarial Attack 33 2.6 Machine Learning Methods That Are Sustainable, Private, and Accountable 36 2.7 Conclusion 40 3 Decentralized, Distributed Computing for Internet of Things-Based Cloud Applications 43 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 44 3.2 Significance of Volunteer Edge Cloud Concept 45 3.3 Proposed System 46 3.4 Implementation of Volunteer Edge Control 49 3.5 Result Analysis of Volunteer Edge Cloud 52 3.6 Introducing Blockchain-Enabled Internet of Things Systems Using the Serverless Cloud Platform 53 3.7 Introducing Serverless Cloud Platforms 54 3.8 Serverless Cloud Platform System Design 55 3.9 Evaluation of HCloud 60 3.10 HCloud-Related Works 61 3.11 Conclusion 62 4 Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications for Next-Generation Society 65 V. Hemamalini, Anand Kumar Mishra, Amit Kumar Tyagi and Vijayalakshmi Kakulapati 4.1 Introduction 65 4.2 Background Work 69 4.3 Motivation 71 4.4 Existing Innovations in the Current Society 72 4.5 Expected Innovations in the Next-Generation Society 72 4.6 An Environment with Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 73 4.7 Open Issues in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 74 4.8 Research Challenges in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 75 4.9 Legal Challenges in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 76 4.10 Future Research Opportunities Towards Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 77 4.11 An Open Discussion 78 4.12 Conclusion 79 5 Artificial Intelligence for Cyber Security: Current Trends and Future Challenges 83 Meghna Manoj Nair, Atharva Deshmukh and Amit Kumar Tyagi 5.1 Introduction: Security and Its Types 83 5.2 Network and Information Security for Industry 4.0 and Society 5.0 86 5.3 Internet Monitoring, Espionage, and Surveillance 89 5.4 Cyber Forensics with Artificial Intelligence and without Artificial Intelligence 91 5.5 Intrusion Detection and Prevention Systems Using Artificial Intelligence 92 5.6 Homomorphic Encryption and Cryptographic Obfuscation 94 5.7 Artificial Intelligence Security as Adversarial Machine Learning 95 5.8 Post-Quantum Cryptography 96 5.9 Security and Privacy in Online Social Networks and Other Sectors 98 5.10 Security and Privacy Using Artificial Intelligence in Future Applications/Smart Applications 99 5.11 Security Management and Security Operations Using Artificial Intelligence for Society 5.0 and Industry 4.0 101 5.12 Digital Trust and Reputation Using Artificial Intelligence 103 5.13 Human-Centric Cyber Security Solutions 104 5.14 Artificial Intelligence-Based Cyber Security Technologies and Solutions 106 5.15 Open Issues, Challenges, and New Horizons Towards Artificial Intelligence and Cyber Security 107 5.16 Future Research with Artificial Intelligence and Cyber Security 109 5.17 Conclusion 110 Part 2: Methods and Techniques 115 6 An Automatic Artificial Intelligence System for Malware Detection 117 Ahmad Moawad, Ahmed Ismail Ebada, A.A. El-Harby and Aya M. Al-Zoghby 6.1 Introduction 117 6.2 Malware Types 119 6.3 Structure Format of Binary Executable Files 121 6.4 Malware Analysis and Detection 124 6.5 Malware Techniques to Evade Analysis and Detection 128 6.6 Malware Detection With Applying AI 130 6.7 Open Issues and Challenges 134 6.8 Discussion and Conclusion 135 7 Early Detection of Darknet Traffic in Internet of Things Applications 139 Ambika N. 7.1 Introduction 139 7.2 Literature Survey 143 7.3 Proposed Work 147 7.4 Analysis of the Work 149 7.5 Future Work 150 7.6 Conclusion 151 8 A Novel and Efficient Approach to Detect Vehicle Insurance Claim Fraud Using Machine Learning Techniques 155 Anand Kumar Mishra, V. Hemamalini, Amit Kumar Tyagi, Piyali Saha and Abirami A. 8.1 Introduction 155 8.2 Literature Survey 156 8.3 Implementation and Analysis 157 8.4 Conclusion 174 9 Automated Secure Computing for Fraud Detection in Financial Transactions 177 Kuldeep Singh, Prasanna Kolar, Rebecca Abraham, Vedantam Seetharam, Sireesha Nanduri and Divyesh Kumar 9.1 Introduction 177 9.2 Historical Perspective 180 9.3 Previous Models for Fraud Detection in Financial Transactions 181 9.4 Proposed Model Based on Automated Secure Computing 182 9.5 Discussion 184 9.6 Conclusion 185 10 Data Anonymization on Biometric Security Using Iris Recognition Technology 191 Aparna D. K., Malarkodi M., Lakshmanaprakash S., Priya R. L. and Ajay Nair 10.1 Introduction 191 10.2 Problems Faced in Facial Recognition 194 10.3 Face Recognition 197 10.4 The Important Aspects of Facial Recognition 199 10.5 Proposed Methodology 201 10.6 Results and Discussion 202 10.7 Conclusion 202 11 Analysis of Data Anonymization Techniques in Biometric Authentication System 205 Harini S., Dharshini R., Agalya N., Priya R. L. and Ajay Nair 11.1 Introduction 205 11.2 Literature Survey 207 11.3 Existing Survey 209 11.4 Proposed System 212 11.5 Implementation of AI 219 11.6 Limitations and Future Works 220 11.7 Conclusion 221 Part 3: Applications 223 12 Detection of Bank Fraud Using Machine Learning Techniques 225 Kalyani G., Anand Kumar Mishra, Diya Harish, Amit Kumar Tyagi, Sajidha S. A. and Shashank Pandey 12.1 Introduction 225 12.2 Literature Review 226 12.3 Problem Description 227 12.4 Implementation and Analysis 228 12.5 Results 238 12.6 Conclusion 238 12.7 Future Works 240 13 An Internet of Things-Integrated Home Automation with Smart Security System 243 Md. Sayeduzzaman, Touhidul Hasan, Adel A. Nasser and Akashdeep Negi 13.1 Introduction 244 13.2 Literature Review 246 13.3 Methodology and Working Procedure with Diagrams 249 13.4 Research Analysis 252 13.5 Establishment of the Prototype 256 13.6 Results and Discussions 265 13.7 Conclusions 270 14 An Automated Home Security System Using Secure Message Queue Telemetry Transport Protocol 275 P. Rukmani, S. Graceline Jasmine, M. Vergin Raja Sarobin, L. Jani Anbarasi and Soumitro Datta 14.1 Introduction 275 14.2 Related Works 277 14.3 Proposed Solution 278 14.4 Implementation 285 14.5 Results 290 14.6 Conclusion and Future Work 292 15 Machine Learning-Based Solutions for Internet of Things-Based Applications 295 Varsha Bhatia and Bhavesh Bhatia 15.1 Introduction 295 15.2 IoT Ecosystem 296 15.3 Importance of Data in IoT Applications 298 15.4 Machine Learning 299 15.5 Machine Learning Algorithms 302 15.6 Applications of Machine Learning in IoT 304 15.7 Challenges of Implementing ML for IoT Solutions 313 15.8 Emerging Trends in IoT 314 15.9 Conclusion 315 16 Machine Learning-Based Intelligent Power Systems 319 Kusumika Krori Dutta, S. Poornima, R. Subha, Lipika Deka and Archit Kamath 16.1 Introduction 319 16.2 Machine Learning Techniques 321 16.3 Implementation of ML Techniques in Smart Power Systems 334 16.4 Case Study 340 16.5 Conclusion 341 Part 4: Future Research Opportunities 345 17 Quantum Computation, Quantum Information, and Quantum Key Distribution 347 Mohanaprabhu D., Monish Kanna S. P., Jayasuriya J., Lakshmanaprakash S., Abirami A. and Amit Kumar Tyagi 17.1 Introduction 347 17.2 Literature Work 352 17.3 Motivation Behind this Study 353 17.4 Existing Players in the Market 354 17.5 Quantum Key Distribution 356 17.6 Proposed Models for Quantum Computing 356 17.7 Simulation/Result 361 17.8 Conclusion 365 18 Quantum Computing, Qubits with Artificial Intelligence, and Blockchain Technologies: A Roadmap for the Future 367 Amit Kumar Tyagi, Anand Kumar Mishra, Aswathy S. U. and Shabnam Kumari 18.1 Introduction to Quantum Computing and Its Related Terms 368 18.2 How Quantum Computing is Different from Security? 374 18.3 Artificial Intelligence—Blockchain-Based Quantum Computing? 375 18.4 Process to Build a Quantum Computer 378 18.5 Popular Issues with Quantum Computing in this Smart Era 379 18.6 Problems Faced with Artificial Intelligence–Blockchain-Based Quantum Computing 379 18.7 Challenges with the Implementation of Quantum Computers in Today's Smart Era 380 18.8 Future Research Opportunities with Quantum Computing 381 18.9 Future Opportunities with Artificial Intelligence–Blockchain-Based Quantum Computing 382 18.10 Conclusion 383 19 Qubits, Quantum Bits, and Quantum Computing: The Future of Computer Security System 385 Harini S., Dharshini R., Praveen R., Abirami A., Lakshmanaprakash S. and Amit Kumar Tyagi 19.1 Introduction 385 19.2 Importance of Quantum Computing 387 19.3 Literature Survey 388 19.4 Quantum Computing Features 390 19.5 Quantum Algorithms 394 19.6 Experimental Results 399 19.7 Conclusion 400 20 Future Technologies for Industry 5.0 and Society 5.0 403 Mani Deepak Choudhry, S. Jeevanandham, M. Sundarrajan, Akshya Jothi, K. Prashanthini and V. Saravanan 20.1 Introduction 404 20.2 Related Work 407 20.3 Comparative Analysis of I4.0 to I5.0 and S4.0 to S5.0 409 20.4 Risks and Prospects 412 20.5 Conclusion 412 21 Futuristic Technologies for Smart Manufacturing: Research Statement and Vision for the Future 415 Amit Kumar Tyagi, Anand Kumar Mishra, Nalla Vedavathi, Vijayalakshmi Kakulapati and Sajidha S. A. 21.1 Introduction About Futuristic Technologies 415 21.2 Related Work Towards Futuristic Technologies 418 21.3 Related Work Towards Smart Manufacturing 419 21.4 Literature Review Towards Futuristic Technology 420 21.5 Motivation 421 21.6 Smart Applications 422 21.7 Popular Issues with Futuristic Technologies for Emerging Applications 424 21.8 Legal Issues Towards Futuristic Technologies 427 21.9 Critical Challenges with Futuristic Technology for Emerging Applications 428 21.10 Research Opportunities for Futuristic Technologies Towards Emerging Applications 430 21.11 Lesson Learned 433 21.12 Conclusion 434 References 434 Index 443
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.
