Automated secure computing for next-generation systems:
Gespeichert in:
| Weitere Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
[2024]
Beverly, MA Scrivener Publishing |
| Ausgabe: | This edition first published |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xix, 445 Seiten Illustrationen, Diagramme |
| ISBN: | 9781394213597 9781394213948 |
Internformat
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Contents Preface Acknowledgements Part 1: Fundamentals 1 2 Digital Twin Technology: Necessity of the Future in Education and Beyond Robertas Damasevicius and Ligita Zailskaité-Jaksté 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 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 xvii xix 1 3 3 5 5 6 7 7 8 8 10 12 13 13 14 15 16 17 18 23 23 24 25 26 27 27 27 28 29
vi 2.4 2.5 2.6 2.7 3 Contents Contents Training in a Differential Environment 2.4.1 Achieving Integrity Inferring in Adversarial Attack 2.5.1 Combatants in the White Box Model 2.5.2 Insurgencies in the Black Box Model 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 Conclusion References 30 31 33 33 35 36 36 38 39 40 40 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 3.3.1 Smart Contract 48 3.3.2 Order Task Method 49 3.3.3 KubeEdge 49 3.4 Implementation of Volunteer Edge Control 49 3.4.1 Formation of a Cloud Environment 50 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 3.7.1 loT Systems 54 3.7.2 JointCloud 54 3.7.3 Computing Without Servers 54 3.7.4 Oracle and Blockchain Technology 55 3.8 Serverless Cloud Platform System Design 55 3.8.1 Aim and Constraints 55 3.8.2 Goals and Challenges 55 3.8.3 HCloud Connections 56 3.8.4 Data Sharing Platform 57 3.8.5 Cloud Manager 57 3.8.6 The Agent 58 3.8.7 Client Library 59 3.8.8 Witness Blockchain 59 3.9 Evaluation of HCloud 60 3.9.1 CPU Utilization 60
3.9.2 Cost Analysis 61 : 3.10 HCloud-Related Works 3.10.1 Serverless 3.10.2 Efficiency 3.11 Conclusion References 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 IntelligenceBlockchain-Enabled-Internet of Things-Based Cloud Applications 4.11 An Open Discussion 4.12 Conclusion References 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 vii 61 61 62 62 63 65 65 69 71 72 72 73 74 75 76 77 78 79 79 83 83 85 86 88 89 89 91 92 94 95 96 98
viii 5.10 5.11 Security and Privacy Using Artificial Intelligence in Future Applications/Smart Applications Security Management and Security Operations Using Artificial Intelligence for Society 5.0 and Industry 4.0 Implementation on the Internet of Things and Protecting Data in loT Connected Devices Digital Trust and Reputation Using Artificial Intelligence Human-Centric Cyber Security Solutions Artificial Intelligence-Based Cyber Security Technologies and Solutions 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 Future Research with Artificial Intelligence and Cyber Security Conclusion References a 8 | 99 j 101 I 5.11.1 5.12 5.13 5.14 5.15 5.16 5.17 Part 2: Methods and Techniques 6 7 102 103 104 106 107 107V 107 108 108 108 108 109 109 110 110 115 An Automatic Artificial Intelligence System for Malware Detection Ahmad Moawad, Ahmed Ismail Ebada, A.A. El-Harby and Aya Μ. 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 Analysisand Detection 6.6 Malware Detection With Applying AI 6.7 Open Issues and Challenges 6.8 Discussion and Conclusion References 117 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 139 117 119 121 124 128 130 134 135 136 139 143 147 148 149 150 151 152 ix Contents ' Contents 9 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 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 Μ., 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 155 155 156 157 157 157 161 162 174 174 174 175 177 177 180 181 181 181 182 182 184 185 186 189 191 191 194 197 199 201 202 202 203
X Contents 11 Analysis of Data Anonymization Techniques in Biometric Authentication System Harini S., Dharshini R., Agafya 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 BiometricSystem 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 Kafyani G., Anand Kumar Mishra, Diya Harish, Amit Kumar Tyagi, Sajidha 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 Contents 205 205 207 209 209 209 210 212 212 213 215 217 217 218 218 218 219 220 221 221 223 225 225 226 227 228 228 228 228 238 238 240 240 243 244 246 249 13.4 13.5 13.6 13.7 Research Analysis Establishment of the Prototype Results and Discussions Conclusions Acknowledgment References 14 An Automated Home Security System Using
Secure Message Queue Telemetry Transport Protocol P. Rukmani, S. Graceline Jasmine, Μ. 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 loT Ecosystem 15.2.1 loT Devices 15.2.2 loT Gateways 15.2.3 loT Platforms 15.2.4 loT Applications 15.2.5 loT Connectivity 15.2.6 Analytics and Data Management 15.2.7 Security and Privacy 15.2.8 Infrastructure 15.3 Importance of Data in loT Applications 15.3.1 Data Gathered from loT Applications 15.3.2 Quality of an loT Application 15.3.3 Effective loT Data Utilization 15.4 Machine Learning 15.4.1 Supervised Learning 15.4.2 Unsupervised Learning 15.4.3 Reinforcement Learning xi 252 256 265 270 271 271 275 275 277 277 277 278 278 278 280 282 285 290 292 293 295 295 296 296 296 297 297 297 297297 298 298 298 298 299 299 300 301 301
xii Contents Contents 15.5 15.6 15.7 15.8 15.9 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 Applications of Machine Learning in loT 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 Challenges of Implementing ML for loT 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 Emerging Trends in loT 15.8.1 Edge Computing 15.8.2 5G and loT 15.8.3 Artificial Intelligence and Machine Learning 15.8.4 Security 15.8.5 Blockchain 15.8.6 loT and Cloud Computing 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 К-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 302 302 302 303 303 303 303 304 304 304 308 309 310 311 311 311 313 313 313 314 314 314 314 314 314 314 314 315 315 315 315 319 319 321 322 322 324 326 326 327 327 327 328 328 16.3 ί.· 16.4 16.5 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 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 Case Study 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. andAmit 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 xiii 328 329 329 329 330 330 332 334 334 334 335 336 337 338 339 340 341 342 343 345 347 347 352 353 354 356 356 361 361 362 363 365 365 367 368 369 369
xiv Contents Contents 18.1.3 Quantum Computation 18.1.4 Quantum Interference 18.1.5 Quantum Information 18.1.6 QuantumSuperposition 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 Todays 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 379 18.7 Challenges with the Implementation of Quantum Computers in Today’s Smart Era 380 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 Chaudhry, S. Jeevanandham, Μ. Sundarrajan, Akshya Jothi, K. Prashanthini and V. Saravanan 20.1 Introduction 20.2 Related Work 370 370 371 371 372 372 373 373 374 374 375 375 376 376 1 П 377 378 379 381 382 383 383 385 385 387 388 390 394 399 400 401 403 404 407 20.3 20.4 20.5 Comparative Analysis of 14.0 to 15.0 and S4.0 to S5.0 Risks and Prospects 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 forEmerging 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 430 21.10.1 Research Statements Towards Futuristic Technologies for Smart Applications 21.10.2 Research Opportunities for Futuristic Technologies Towards Smart Manufacturing 432 21.11
Lesson Learned 21.12 Conclusion References Index xv 409 412 412 413 413 415 415 418 419 420 421 422 424 425 426 427 428 429 429 431 433 434 434 443 |
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| illustrated | Illustrated |
| indexdate | 2024-08-01T00:19:57Z |
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| isbn | 9781394213597 9781394213948 |
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| spelling | Automated secure computing for next-generation systems edited by Amit Kumar Tyagi This edition first published Hoboken, NJ Wiley [2024] Beverly, MA Scrivener Publishing xix, 445 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Tyagi, Amit Kumar 1988- (DE-588)1231503025 edt Erscheint auch als Online-Ausgabe 978-1-394-21394-8 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035066261&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Automated secure computing for next-generation systems Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4193754-5 |
| title | Automated secure computing for next-generation systems |
| title_auth | Automated secure computing for next-generation systems |
| title_exact_search | Automated secure computing for next-generation systems |
| title_full | Automated secure computing for next-generation systems edited by Amit Kumar Tyagi |
| title_fullStr | Automated secure computing for next-generation systems edited by Amit Kumar Tyagi |
| title_full_unstemmed | Automated secure computing for next-generation systems edited by Amit Kumar Tyagi |
| title_short | Automated secure computing for next-generation systems |
| title_sort | automated secure computing for next generation systems |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Künstliche Intelligenz Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035066261&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT tyagiamitkumar automatedsecurecomputingfornextgenerationsystems |