Overview

Fortify your digital defenses with this essential book, which provides a roadmap for moving beyond the limitations of traditional encryption by leveraging generative AI algorithms to proactively anticipate, detect, and mitigate the next generation of cyber threats in real-time. In recent years, encryption has shown limitations as the sole safeguard against cyber threats in an increasingly interconnected world. While encryption remains a crucial component of cybersecurity, it is no longer sufficient to combat the evolving tactics of malicious actors. This book advocates for a paradigm shift towards leveraging generative AI algorithms to anticipate, detect, and mitigate emerging threats in real-time. Through detailed case studies and practical examples, the book illustrates how these AI-driven approaches can augment traditional security measures, providing organizations with a proactive defense against cyberattacks. It explores the connections between artificial intelligence and cybersecurity, exploring how generative AI technologies can revolutionize security paradigms beyond traditional encryption methods. Authored by leading experts in both AI and cybersecurity, the book presents a comprehensive examination of the challenges facing modern digital security and proposes innovative solutions grounded in generative AI. By combining theoretical frameworks with actionable insights, this book serves as a roadmap for organizations looking to fortify their defenses in an era of unprecedented cyber threats, making it an essential resource for anyone invested in the evolving landscape of cybersecurity and AI.

Full Product Details

Author:   Santosh Kumar Srivastava (GL Bajaj Institute of Technology and Management, India) ,  Durgesh Srivastava (Chitkara University, India) ,  Manoj Kumar Mahto (BRCM College of Engineering and Technology, India) ,  Ben Othman Soufiane (Higher Institute of Informatics and Techniques of Communication (ISTIC), Tunisia)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Scrivener
ISBN:  

9781394305643

ISBN 10:   1394305648
Pages:   496
Publication Date:   16 January 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

Preface xix 1 Introduction to Generative Artificial Intelligence 1 Ch Raja Ramesh, P. Muralidhar, K. M. V. Madan Kumar, B. Srinu, G. Raja Vikram and Rakesh Nayak 1.1 Introduction 2 1.2 Historical Context 3 1.3 Fundamental Architecture of Generative AI 5 1.3.1 Data Processing Layer 5 1.3.2 Generative Model Layer 6 1.3.3 Improvement and Feedback Layer 7 1.3.4 Integration and Deployment Layer 8 1.4 Applications of Generative AI 8 1.5 Ethical Implications 10 1.6 Societal Implications 12 1.7 Use Cases in Generative AI 14 1.8 Education 14 1.9 Health Care 15 1.10 Challenges in Generative AI 17 1.11 Challenges in Education 17 1.12 Challenges in Health Care 18 1.13 Future Directions 19 1.14 Interpretable and Controllable Generative AI 20 1.15 Collaboration between AI and Human Creativity 21 1.16 Conclusion 21 References 22 2 Deep Learning in Cyber Security: A Guide to Harnessing Generative AI for Enhanced Threat Detection 25 P. Lavanya Kumari, Rajendra Prasad, Sai Teja Inampudi, Nagaram Nagarjuna and Vishesh Chawan 2.1 Introduction 26 2.1.1 Overview of Cyber Security 26 2.1.2 Role of AI in Cyber Security 27 2.1.3 Introduction to Deep Learning and Generative AI 28 2.2 Deep Learning Basics 28 2.2.1 Understanding Neural Networks 28 2.2.2 Types of Deep Learning Models 30 2.2.3 Training Deep Learning Models 31 2.3 Generative AI 32 2.3.1 Understanding Generative Models 32 2.3.2 Applications of Generative AI 33 2.3.3 Generative AI in Cyber Security 35 2.4 Enhancing Threat Detection with Generative AI 37 2.4.1 Current Challenges in Threat Detection 37 2.4.2 How Generative AI Enhances Threat Detection 38 2.4.3 Case Studies of Generative AI in Threat Detection 39 2.5 Implementing Generative AI for Threat Detection 40 2.5.1 Preparing Your Data 40 2.5.2 Building a Generative Model 41 2.5.3 Evaluating Model Performance 42 2.6 Future Trends in AI-Driven Cyber Security 43 2.6.1 Emerging Trends 43 2.6.2 Potential Challenges 43 2.7 Conclusion 44 References 45 3 Cognitive Firewalls: Reinventing Cybersecurity through Generative Models 49 Ramandeep Kaur and Santosh Kumar Srivastava 3.1 Introduction 50 3.1.1 Cybersecurity’s Significance 50 3.1.2 Value of Cyber Threats 51 3.1.3 Introduction to Generative AI and Deep Learning in Cyber Security 51 3.1.4 Goal of the Chapter 53 3.2 Basics of Deep Learning 53 3.2.1 Overview of Machine Learning & Deep Learning 53 3.2.2 Important Ideas: Neural Networks (NNs), Layers and Activation Functions 54 3.2.3 Deep Learning Architectures: CNN, RNN, and GANs 55 3.3 Synopsis of Cybersecurity 56 3.3.1 Awareness of Cyber Threats: DDoS, Phishing, and Malware 57 3.3.2 Customary Cybersecurity Tools: Firewalls, Antivirus Software, and IDS/IPS 57 3.3.3 Restrictions on Conventional Methods 58 3.3.4 The Function of Artificial Intelligence in Cybersecurity 58 3.4 Cybersecurity and Generative AI 60 3.4.1 Overview of Generative AI: GAN and VAE 60 3.4.2 How Generative AI is Different from Other AI Methods 60 3.4.3 Cyber Security’s Potential Applications 62 3.4.4 Ethical Issues and Challenges 63 3.5 Enhanced Threat Detection Using Generative AI 64 3.5.1 Techniques for Anomaly Detection 64 3.5.2 Real-Time Threat Detection with Generative AI 65 3.6 Execution Techniques 67 3.6.1 Building a Cyber Security Generative AI Model 67 3.6.2 Gathering and Preparing Data 68 3.6.3 Testing and Training of Models 70 3.6.4 Deployment Considerations 71 3.7 Case Research and Utilization 72 3.7.1 Applications of Generative AI in Cybersecurity in the Real World 72 3.7.2 Success Stories and Lessons Learned 73 3.7.3 Comparison with Routine Methodologies 75 3.8 Prospective Patterns and Directions 77 3.8.1 New Developments in Cybersecurity and Deep Learning 77 3.8.2 Future Directions for Generative AI in Threat Detection 78 3.8.3 Prospective Fields of Study 79 3.9 Key Findings 80 3.10 Conclusion 80 References 81 4 Biometric Fusion: Exploring Generative AI Applications in Multi-Modal Security Systems 85 Suryakanta, Ritu, Anu Rani, Neerja Negi, Surya Kant Pal and Kamalpreet Singh Bhangu 4.1 Introduction 86 4.2 Literature Review 88 4.3 Overview of Multi-Modal Biometric Security Systems 93 4.4 Generative AI in Multi-Modal Biometric Security 94 4.5 Benefits of Generative AI in Multi-Modal Biometric Systems 97 4.6 Challenges and Ethical Considerations 99 4.7 Future Directions 100 4.8 Conclusion 103 References 104 5 Dynamic Threat Intelligence: Leveraging Generative AI for Real-Time Security Response 107 Manoj Kumar Mahto 5.1 Introduction 108 5.1.1 The Evolving Threat Landscape 108 5.1.2 Importance of Real-Time Security Response 109 5.1.3 Role of Generative AI in Modern Cybersecurity 110 5.2 Fundamentals of Threat Intelligence 111 5.2.1 Definition and Types of Threat Intelligence 111 5.2.2 Traditional vs. Dynamic Threat Intelligence 112 5.2.3 Challenges in Current Threat Intelligence Systems 112 5.3 Generative AI in Cybersecurity 113 5.3.1 Overview of Generative AI Technologies 113 5.3.2 Use Cases in Cybersecurity: From Threat Detection to Response 114 5.3.3 Strengths and Limitations of Generative AI 115 5.3.3.1 Strengths of Generative AI in Cybersecurity 115 5.3.3.2 Limitations of Generative AI in Cybersecurity 116 5.4 Architecture for Dynamic Threat Intelligence 116 5.4.1 Key Components of a Generative AI-Driven Security System 118 5.4.2 Integration with Existing Security Infrastructure 119 5.4.3 Real-Time Data Processing and Threat Correlation 120 5.5 Applications and Use Cases 120 5.6 Techniques for Leveraging Generative AI 123 5.6.1 Natural Language Processing (NLP) for Threat Intelligence 124 5.6.2 Synthetic Data Generation for Cybersecurity Simulations 125 5.6.3 Real-Time Incident Response Automation 125 5.7 Addressing Ethical and Privacy Concerns 126 5.7.1 Ethical Considerations in AI-Powered Security 127 5.7.2 Managing Bias in Generative AI Models 127 5.7.3 Ensuring Privacy in Threat Intelligence Data 128 5.8 Case Studies and Real-World Implementations 128 5.9 Future Directions in Threat Intelligence 131 5.9.1 Advances in Generative AI for Cybersecurity 132 5.9.2 The Role of Explainable AI in Threat Response 133 5.9.3 Long-Term Trends and Challenges 133 5.10 Conclusion 134 References 135 6 Cognitive Security: Integrating Generative AI for Adaptive and Self-Learning Defenses 137 Akruti Sinha, Akshet Patel and Deepak Sinwar 6.1 Introduction 138 6.2 Cognitive Security and Human Vulnerabilities 140 6.2.1 Definition 140 6.2.2 Human Role in Cognitive Security Including Vulnerability 141 6.2.3 Attacks and Attacker’s Strategies 145 6.3 GenAI in Security 147 6.4 Self-Learning Systems in Cognitive Security 149 6.4.1 Anomaly Detection and Threat Identification 150 6.4.2 Automated Response and Mitigation 150 6.4.3 Continuous Learning and Adaptation 150 6.4.4 Enhanced Decision Support 150 6.5 Predictive Security Analytics with Generative Models 152 6.6 AI-Driven Incident Response and Remediation 155 6.7 Ethical Perspective 158 6.8 Security Considerations 159 6.9 Mitigation Strategies 160 6.10 Conclusion 161 References 162 7 Quantum Computing and Generative AI: Securing the Future of Information 167 Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik and Praveen Kantha 7.1 Introduction 168 7.2 Foundations of Quantum Computing 171 7.3 Quantum Algorithms 174 7.4 Current Landscape of Quantum Computing 179 7.5 Generative AI: Understanding the Technology 182 7.6 Quantum-Inspired Generative AI 183 7.7 Synergies and Challenges 184 7.8 Applications and Future Prospects 185 7.9 Case Studies and Success Stories 186 7.10 Result 187 7.11 Conclusion 190 References 191 8 Blockchain-Enabled Smart City Solutions: Exploring the Technology’s Evolution and Applications 195 Pratiksh Lalitbhai Khakhariya, Sushil Kumar Singh, Ravikumar R. N. and Deepak Kumar Verma 8.1 Introduction 196 8.2 Related Work 198 8.2.1 Preliminaries 199 8.2.1.1 Smart Cities 199 8.2.1.2 Blockchain Technology 200 8.2.1.3 IoT Technology and Architecture 202 8.3 Blockchain-Based Secure Architecture for IoT-Enabled Smart Cities 206 8.3.1 Overview of IoT-Enabled Smart Cities Using Blockchain Technology 206 8.3.2 Security Issues and Solutions 212 8.4 Open Research Challenges and Future Directions 213 8.4.1 Open Research Challenges 214 8.4.2 Future Directions 215 8.5 Conclusion 220 Acknowledgment 220 References 220 Contents xi 9 Human-Centric Security: The Role of Generative AI in User Behavior Analysis 227 Sunil Sharma, Priyajit Dash, Bhupendra Soni and Yashwant Singh Rawal 9.1 Introduction to Human-Centric Security and Generative AI 228 9.1.1 Human-Centric Security: An Evolving Paradigm 228 9.1.1.1 The Role of Generative AI 228 9.1.1.2 The Evolution of AI in Security 229 9.1.1.3 What is Generative AI 229 9.1.1.4 Benefits of Generative AI in Security 229 9.1.1.5 Applications of Generative AI in Security 230 9.2 Importance of User Behavior Analysis 231 9.2.1 Enhancing Security through Behavioral Insights 231 9.2.2 Supporting Fraud Detection and Prevention 232 9.2.3 Improving User Authentication 232 9.2.4 Enhancing User Experience and Trust 233 9.2.5 Enabling Proactive Security Measures 233 9.3 Behavioral Biometrics Enhanced by Generative AI 234 9.3.1 Introduction to Behavioral Biometrics 234 9.3.2 Fundamental Principles of Behavioral Biometrics 234 9.3.3 Integrating Generative AI with Behavioral Biometrics 236 9.3.4 Enhancing Accuracy and Reliability 236 9.4 Formulating User-Centric Security Policies 237 9.4.1 Challenges in Policy Formulation 238 9.4.2 AI’s Role in Policy Adaptation and Implementation 239 9.4.3 Ethical Considerations and User Privacy 241 9.5 Human-AI Collaboration in Security Frameworks 242 9.5.1 Key Components of Human-AI Collaboration 242 9.5.2 Models of Human-AI Interaction 243 9.5.3 Experimental Workflow and Findings 244 9.6 Future Trends in Collaborative Security 247 9.7 Challenges and Future Directions 248 9.7.1 Technical Challenges 249 9.7.2 Anticipating Future Threat Landscapes 250 9.7.3 Human-AI Collaborative Defense 252 9.8 Conclusion 253 References 253 10 Human Centric Security: Human Behavior Analysis Based on GenAI 257 P. Muralidhar, Ch. Raja Ramesh, V. K. S. K. Sai Vadapalli and Bh. Lakshmi Madhuri 10.1 Introduction 258 10.2 Model of ChatGPT 259 10.3 Human Interaction with ChatGPT 261 10.4 Impact of GAI in Cyber Security 262 10.5 Attacks Enhanced by GAI 263 10.6 Replicate Version of ChatGPT 265 10.6.1 Vulnerabilities of GAI Models 265 10.6.2 Road Map of GAI in Cybersecurity and Privacy 266 10.7 Enhancement of Destructions with ChatGPT 271 10.8 Protection Measures Using GAI Models 274 10.8.1 Cyber Security Reporting 274 10.8.2 Generating Secure Code Using ChatGPT 274 10.8.3 Detection the Cyber Attacks 274 10.8.4 Improving Ethical Guidelines 274 10.9 GAI Tools to Boost Security 275 10.10 Future Trends and Challenges 276 10.11 Conclusion 277 References 277 11 Machine Learning-Based Malicious Web Page Detection Using Generative AI 281 Ashwini Kumar, Harikesh Singh, Mayank Singh and Vimal Gupta 11.1 Introduction 282 11.1.1 Background and Motivation 282 11.1.2 Threat Landscape: Rise of Malicious Web Pages 284 11.1.3 Role of ML and GenAI in Cybersecurity 285 11.1.4 Objectives of the Chapter 286 11.2 Related Work 287 11.2.1 Signature-Based Detection Systems 287 11.2.2 Heuristic and Rule-Based Techniques 288 11.2.3 Traditional ML Approaches: SVM, Decision Trees, Random Forests 288 11.2.4 Deep Learning for Web Page Classification 289 11.2.5 Recent Advances in GenAI for Cybersecurity 290 11.2.6 Comparative Analysis of Approaches 291 11.3 Methodology 292 11.3.1 Data Collection and Preprocessing 292 11.3.2 Feature Engineering 292 11.3.3 Machine Learning Models 293 11.3.4 Integrating Generative AI 293 11.3.5 Hybrid Detection Architecture 293 11.4 Experimental Evaluation 294 11.4.1 Datasets 294 11.4.2 Preprocessing and Feature Extraction 294 11.4.3 Experimental Setup 295 11.4.4 Evaluation Metrics 296 11.4.5 Results 296 11.5 Challenges and Limitations 297 11.5.1 Evasion Techniques and Obfuscation 298 11.5.2 Data Quality and Labeling 298 11.5.3 Generalization and Domain Adaptation 298 11.5.4 Dual-Use Nature of Generative AI 299 11.5.5 Explainability and Interpretability 299 11.6 Conclusion 300 11.7 Future Directions 300 11.7.1 Adaptive and Continual Learning 301 11.7.2 Multi-Modal Threat Analysis 301 11.7.3 Explainable AI (XAI) in Detection Pipelines 301 11.7.4 Federated and Privacy-Preserving Learning 302 11.7.5 Responsible Use of Generative AI 302 References 302 12 A Comprehensive Survey of the 6G Network Technologies: Challenges, Possible Attacks, and Future Research 305 Riddhi V. Harsora, Sushil Kumar Singh, Ravikumar R. N., Deepak Kumar Verma and Santosh Kumar Srivastava 12.1 Introduction 306 12.2 Related Work 308 12.2.1 6G Necessities 310 12.2.1.1 Virtualization Security Solution 310 12.2.1.2 Automated Management System 311 12.2.1.3 Users’ Privacy-Preservation 311 12.2.1.4 Data Security Using AI 311 12.2.1.5 Post-Quantum Cryptography 311 12.2.1.6 Security Issues and Solutions 312 12.2.1.7 Low-Latency Communication 312 12.2.1.8 Terahertz Communication 314 12.2.1.9 Quantum-Safe Encryption 314 12.2.1.10 Privacy-Preserving Techniques 314 12.2.1.11 Reliability and Resilience 315 12.2.1.12 Authentication and Authorization 315 12.2.1.13 AI-Driven Network Optimization 315 12.2.1.14 Malware and Cyber Attacks 315 12.3 6G Security: Possible Attacks and Solutions on Emerging Technologies 316 12.3.1 Physical Layer Security 316 12.3.1.1 Visible Light Communication Technology 317 12.3.1.2 Terahertz Technology 318 12.3.1.3 Molecular Communication 320 12.3.2 ABC Security 321 12.3.2.1 Artificial Intelligence 322 12.3.2.2 Blockchain 324 12.3.2.3 Quantum Communication 326 12.4 6G Survey Scenario and Future Scope 327 12.4.1 6G Survey Scenario 327 12.4.2 6G Future Scope 328 12.5 Conclusion 329 Acknowledgment 330 References 330 13 RDE-GAI-IDS: Real-Time Distributed Ensemble and Generative-AI-Based Intrusion Detection System to Detect Threats in Edge Computing Networks 335 Amit Kumar, Vivek Kumar, Manoj Kumar Mahto and Abhay Pratap Singh Bhadauria 13.1 Introduction 336 13.2 Related Work 338 13.3 Proposed Methodology 340 13.3.1 Dataset Description 341 13.3.2 Data Integration 341 13.3.3 Data Pre-Processing (DP) 342 13.3.4 Remove Missing and Infinite Feature Values 342 13.3.5 Data Normalization 342 13.3.6 Feature Selection 343 13.3.7 Generative Artificial Intelligence (GAI) 343 13.4 Constructing the Model 348 13.4.1 RF Algorithm 348 13.4.2 DT Algorithm 349 13.4.3 ET Algorithm 349 13.4.4 KNN Algorithm 349 13.4.5 Training and Testing 351 13.5 Experimental Results & Discussion 351 13.5.1 Performance Evaluation Criteria 351 13.5.2 Comparison with Previous Methods 353 13.6 Conclusion 356 References 356 14 Leveraging Generative AI for Advanced Threat Detection in Cybersecurity 359 Anuradha Reddy, Mamatha Kurra, G. S. Pradeep Ghantasala and Pellakuri Vidyullatha 14.1 Introduction 360 14.2 Purpose 361 14.3 Scope 362 14.4 History 363 14.5 Applications in Industry 367 14.6 Applications in Defense 369 14.6.1 Leveraging Generative AI for Advanced Threat Detection in Cybersecurity in Banking 370 14.6.2 Leveraging Generative AI for Advanced Threat Detection in Cybersecurity in Military Applications 372 14.6.3 Leveraging Generative AI for Advanced Threat Detection in Cybersecurity in Health Care Applications 374 14.7 Challenges and Considerations 376 14.7.1 Future Trends and Directions 378 14.8 Conclusion 381 References 382 15 Quantum Computing and Generative AI-Securing the Future of Information 383 Deeya Shalya, Rimon Ranjit Das and Gurpreet Kaur 15.1 Introduction 384 15.2 Generative AI-Enabled Intelligent Resource Allocation for Quantum Computing Networks 388 15.3 The Synergy of Two Worlds: Bridging Classical and Quantum Computing in Hybrid Quantum-Classical Machine Learning Models 391 15.3.1 The Collaborative Approach 393 15.3.2 Real-World Application 393 15.4 Generative AI in Medical Practice: Privacy and Security Challenges 394 15.4.1 Introduction 394 15.5 Quantum Machine Learning 398 15.5.1 Background 398 15.5.2 Complexity 402 15.6 qGAN-Quantum Generative Adversarial Network 404 15.6.1 Linear-Algebra Based Quantum Machine Learning 405 15.6.1.1 Quantum Principal Component Analysis 406 15.6.1.2 Quantum Support Vector Machines and Kernel Methods 407 15.6.1.3 qBLAS Based Optimization 408 15.6.1.4 NT Angled Datasets for Quantum Machine Learning 410 15.6.2 Reading Classical Data into Quantum Machines 411 15.6.3 Deep Quantum Learning 412 15.6.4 Quantum Machine Learning for Quantum Data 414 15.7 The Impact of the NISQ Era on Quantum Computing and Generative AI 415 15.7.1 Quantum Machine Learning in the NISQ Era 417 15.7.2 Quantum Convolution Neural Network 419 15.8 Conclusion and Future Scope 420 15.8.1 Challenges in Resource Allocation for Quantum Computing Networks 421 15.8.2 Barren Plateaus 422 Acknowledgements 424 References 425 Bibliography 428 16 Redefining Security: Significance of Generative AI and Difficulties of Conventional Encryption 431 R. Nandhini, Gaurab Mudbhari and S. Prince Sahaya Brighty 16.1 Introduction 432 16.1.1 Encryption’s Significance in Cybersecurity 433 16.2 Traditional Encryption Techniques 434 16.2.1 Different Encryption Method Types 434 16.2.1.1 Symmetric Encryption 435 16.2.1.2 Asymmetric Encryption 435 16.2.1.3 Hash Functions 435 16.2.2 Challenges and Limitations of Conventional Encryption 436 16.2.2.1 Brute-Force Attacks 436 16.2.2.2 Issue in Key Management 436 16.2.2.3 Blind Spots in Anomaly Detection 436 16.3 Introduction to Generative AI 437 16.3.1 Unimodal (CV & NLP) 437 16.3.2 Combining Different Modes—Visual and Linguistic 438 16.3.3 The Potential of Generative AI for Data Simulation 439 16.3.3.1 Beneficial Patterns in the Data 440 16.3.3.2 User Behavior Modeling 440 16.4 Applications of Generative AI in Cybersecurity 440 16.4.1 Deceptive Honeypots 441 16.4.2 Dynamic Defense Systems 441 16.4.3 An Application of Generative AI in E-Commerce Platforms and to Update Its Adaptive Data Systems 442 16.4.4 Adaptive Data System Updates 442 16.4.5 Predictive Threat Identification 442 16.4.6 Behavioral Biometrics for Anomaly Detection 442 16.4.7 Enhanced User Authentication Systems 443 16.5 Problems in Implementing Generative AI 443 16.5.1 Algorithm Fairness and Bias 443 16.5.2 Ensuring Equitable AI Decisions 444 16.5.3 Taking on Malevolent AI Models 444 16.5.4 Technical Resource Demands for Generative AI 445 16.6 Combining Generative AI with Traditional Methods 445 16.6.1 Hybrid Security Models 446 16.7 Emerging Trends in AI and Security: A Double-Edged Sword 446 16.7.1 AI-Powered Attacks 446 16.7.1.1 AI in Defense: Strengthening the Cybersecurity Barrier 446 16.7.1.2 Explainable AI (XAI): Establishing Transparency and Trust 447 16.7.1.3 Generative AI: A Powerful Tool with Potential Risks 447 16.8 Conclusion 447 References 448 Index 453

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Santosh Kumar Srivastava, PhD is an Associate Professor in the Department of Applied Computational Science and Engineering at the GL Bajaj Institute of Technology and Management with more than 21 years of experience. He has published more than 15 papers in reputed national and international journals and conferences and five patents. He is a distinguished researcher in the areas of computer networking, wireless technology, network security, and cloud computing. Durgesh Srivastava, PhD is an Associate Professor in the Chitkara University Institute of Engineering and Technology at Chitkara University with more than 14 years of academic and research experience. He has published more than 30 papers in reputed national and international journals and conferences, as well as several books and patents. His research interests include machine learning, soft computing, pattern recognition, and software engineering, modeling, and design. Manoj Kumar Mahto, PhD is an Assistant Professor at BRCM College of Engineering and Technology. Bahal, Haryana, India. He has published more than 15 journal articles, ten book chapters, and three patents. His research interests encompass AI and machine learning, image processing, and natural language processing. Ben Othman Soufiane, PhD works in the Programming and Information Center Research Laboratory associated with the Higher Institute of Informatics and Techniques of Communication. He has published more than 70 papers in reputed international journals, conferences, and book chapters. His research focuses on the Internet of Medical Things, wireless body sensor networks, wireless networks, artificial intelligence, machine learning, and big data. Praveen Kantha, PhD is an Associate Professor in the School of Engineering and Technology at Chitkara University. He is the author of 20 research papers published in national and international journals and conferences, several book chapters, and two patents. His research interests include machine learning, intrusion detection, big data analytics, and autonomous and connected vehicles.

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