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Generative Artificial Intelligence for Next Generation Security Paradigms

Edited by Santosh Kumar Srivastava, Durgesh Srivastava, Manoj Kumar Mahto, Ben Othman Soufiane, and Praveen Kantha
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394305643  |  Hardcover  |  
482 pages
Price: $225 USD
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One Line Description
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.

Audience
Students, teachers, researchers, and industry professionals involved in computer science and IT looking to the future of network security.

Description
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.

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Author / Editor Details
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 Soufiene, 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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Table of Contents
Preface
1. Introduction to Generative Artificial Intelligence
Ch Raja Ramesh, P. Muralidhar, K. M. V. Madan Kumar, B. Srinu, G. Raja Vikram and Rakesh Nayak
1.1 Introduction
1.2 Historical Context
1.3 Fundamental Architecture of Generative AI
1.3.1 Data Processing Layer
1.3.2 Generative Model Layer
1.3.3 Improvement and Feedback Layer
1.3.4 Integration and Deployment Layer
1.4 Applications of Generative AI
1.5 Ethical Implications
1.6 Societal Implications
1.7 Use Cases in Generative AI
1.8 Education
1.9 Health Care
1.10 Challenges in Generative AI
1.11 Challenges in Education
1.12 Challenges in Health Care
1.13 Future Directions
1.14 Interpretable and Controllable Generative AI
1.15 Collaboration between AI and Human Creativity
1.16 Conclusion
References
2. Deep Learning in Cyber Security: A Guide to Harnessing Generative AI for Enhanced Threat Detection
P. Lavanya Kumari, Rajendra Prasad, Sai Teja Inampudi, Nagaram Nagarjuna and Vishesh Chawan
2.1 Introduction
2.1.1 Overview of Cyber Security
2.1.2 Role of AI in Cyber Security
2.1.3 Introduction to Deep Learning and Generative AI
2.2 Deep Learning Basics
2.2.1 Understanding Neural Networks
2.2.2 Types of Deep Learning Models
2.2.3 Training Deep Learning Models
2.3 Generative AI
2.3.1 Understanding Generative Models
2.3.2 Applications of Generative AI
2.3.3 Generative AI in Cyber Security
2.4 Enhancing Threat Detection with Generative AI
2.4.1 Current Challenges in Threat Detection
2.4.2 How Generative AI Enhances Threat Detection
2.4.3 Case Studies of Generative AI in Threat Detection
2.5 Implementing Generative AI for Threat Detection
2.5.1 Preparing Your Data
2.5.2 Building a Generative Model
2.5.3 Evaluating Model Performance
2.6 Future Trends in AI-Driven Cyber Security
2.6.1 Emerging Trends
2.6.2 Potential Challenges
2.7 Conclusion
References
3. Cognitive Firewalls: Reinventing Cybersecurity through Generative Models
Ramandeep Kaur and Santosh Kumar Srivastava
3.1 Introduction
3.1.1 Cybersecurity’s Significance
3.1.2 Value of Cyber Threats
3.1.3 Introduction to Generative AI and Deep Learning in Cyber Security
3.1.4 Goal of the Chapter
3.2 Basics of Deep Learning
3.2.1 Overview of Machine Learning & Deep Learning
3.2.2 Important Ideas: Neural Networks (NNs), Layers and Activation Functions
3.2.3 Deep Learning Architectures: CNN, RNN, and GANs
3.3 Synopsis of Cybersecurity
3.3.1 Awareness of Cyber Threats: DDoS, Phishing, and Malware
3.3.2 Customary Cybersecurity Tools: Firewalls, Antivirus Software, and IDS/IPS
3.3.3 Restrictions on Conventional Methods
3.3.4 The Function of Artificial Intelligence in Cybersecurity
3.4 Cybersecurity and Generative AI
3.4.1 Overview of Generative AI: GAN and VAE
3.4.2 How Generative AI is Different from Other AI Methods
3.4.3 Cyber Security’s Potential Applications
3.4.4 Ethical Issues and Challenges
3.5 Enhanced Threat Detection Using Generative AI
3.5.1 Techniques for Anomaly Detection
3.5.2 Real-Time Threat Detection with Generative AI
3.6 Execution Techniques
3.6.1 Building a Cyber Security Generative AI Model
3.6.2 Gathering and Preparing Data
3.6.3 Testing and Training of Models
3.6.4 Deployment Considerations
3.7 Case Research and Utilization
3.7.1 Applications of Generative AI in Cybersecurity in the Real World
3.7.2 Success Stories and Lessons Learned
3.7.3 Comparison with Routine Methodologies
3.8 Prospective Patterns and Directions
3.8.1 New Developments in Cybersecurity and Deep Learning
3.8.2 Future Directions for Generative AI in Threat Detection
3.8.3 Prospective Fields of Study
3.9 Key Findings
3.10 Conclusion
References
4. Biometric Fusion: Exploring Generative AI Applications in Multi-Modal Security Systems
Suryakanta, Ritu, Anu Rani, Neerja Negi, Surya Kant Pal and Kamalpreet Singh Bhangu
4.1 Introduction
4.2 Literature Review
4.3 Overview of Multi-Modal Biometric Security Systems
4.4 Generative AI in Multi-Modal Biometric Security
4.5 Benefits of Generative AI in Multi-Modal Biometric Systems
4.6 Challenges and Ethical Considerations
4.7 Future Directions
4.8 Conclusion
References
5. Dynamic Threat Intelligence: Leveraging Generative AI for Real-Time Security Response
Manoj Kumar Mahto
5.1 Introduction
5.1.1 The Evolving Threat Landscape
5.1.2 Importance of Real-Time Security Response
5.1.3 Role of Generative AI in Modern Cybersecurity
5.2 Fundamentals of Threat Intelligence
5.2.1 Definition and Types of Threat Intelligence
5.2.2 Traditional vs. Dynamic Threat Intelligence
5.2.3 Challenges in Current Threat Intelligence Systems
5.3 Generative AI in Cybersecurity
5.3.1 Overview of Generative AI Technologies
5.3.2 Use Cases in Cybersecurity: From Threat Detection to Response
5.3.3 Strengths and Limitations of Generative AI
5.3.3.1 Strengths of Generative AI in Cybersecurity
5.3.3.2 Limitations of Generative AI in Cybersecurity
5.4 Architecture for Dynamic Threat Intelligence
5.4.1 Key Components of a Generative AI-Driven Security System
5.4.2 Integration with Existing Security Infrastructure
5.4.3 Real-Time Data Processing and Threat Correlation
5.5 Applications and Use Cases
5.6 Techniques for Leveraging Generative AI
5.6.1 Natural Language Processing (NLP) for Threat Intelligence
5.6.2 Synthetic Data Generation for Cybersecurity Simulations
5.6.3 Real-Time Incident Response Automation
5.7 Addressing Ethical and Privacy Concerns
5.7.1 Ethical Considerations in AI-Powered Security
5.7.2 Managing Bias in Generative AI Models
5.7.3 Ensuring Privacy in Threat Intelligence Data
5.8 Case Studies and Real-World Implementations
5.9 Future Directions in Threat Intelligence
5.9.1 Advances in Generative AI for Cybersecurity
5.9.2 The Role of Explainable AI in Threat Response
5.9.3 Long-Term Trends and Challenges
5.10 Conclusion
References
6. Cognitive Security: Integrating Generative AI for Adaptive and Self-Learning Defenses
Akruti Sinha, Akshet Patel and Deepak Sinwar
6.1 Introduction
6.2 Cognitive Security and Human Vulnerabilities
6.2.1 Definition
6.2.2 Human Role in Cognitive Security Including Vulnerability
6.2.3 Attacks and Attacker’s Strategies
6.3 GenAI in Security
6.4 Self-Learning Systems in Cognitive Security
6.4.1 Anomaly Detection and Threat Identification
6.4.2 Automated Response and Mitigation
6.4.3 Continuous Learning and Adaptation
6.4.4 Enhanced Decision Support
6.5 Predictive Security Analytics with Generative Models
6.6 AI-Driven Incident Response and Remediation
6.7 Ethical Perspective
6.8 Security Considerations
6.9 Mitigation Strategies
6.10 Conclusion
References
7. Quantum Computing and Generative AI: Securing the Future of Information
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik and Praveen Kantha
7.1 Introduction
7.2 Foundations of Quantum Computing
7.3 Quantum Algorithms
7.4 Current Landscape of Quantum Computing
7.5 Generative AI: Understanding the Technology
7.6 Quantum-Inspired Generative AI
7.7 Synergies and Challenges
7.8 Applications and Future Prospects
7.9 Case Studies and Success Stories
7.10 Result
7.11 Conclusion
References
8. Blockchain-Enabled Smart City Solutions: Exploring the Technology’s Evolution and Applications
Pratiksh Lalitbhai Khakhariya, Sushil Kumar Singh, Ravikumar R. N. and Deepak Kumar Verma
8.1 Introduction
8.2 Related Work
8.2.1 Preliminaries
8.2.1.1 Smart Cities
8.2.1.2 Blockchain Technology
8.2.1.3 IoT Technology and Architecture
8.3 Blockchain-Based Secure Architecture for IoT-Enabled Smart Cities
8.3.1 Overview of IoT-Enabled Smart Cities Using Blockchain Technology
8.3.2 Security Issues and Solutions
8.4 Open Research Challenges and Future Directions
8.4.1 Open Research Challenges
8.4.2 Future Directions
8.5 Conclusion
Acknowledgment
References
9. Human-Centric Security: The Role of Generative AI in User Behavior Analysis
Sunil Sharma, Priyajit Dash, Bhupendra Soni and Yashwant Singh Rawal
9.1 Introduction to Human-Centric Security and Generative AI
9.1.1 Human-Centric Security: An Evolving Paradigm
9.1.1.1 The Role of Generative AI
9.1.1.2 The Evolution of AI in Security
9.1.1.3 What is Generative AI
9.1.1.4 Benefits of Generative AI in Security
9.1.1.5 Applications of Generative AI in Security
9.2 Importance of User Behavior Analysis
9.2.1 Enhancing Security through Behavioral Insights
9.2.2 Supporting Fraud Detection and Prevention
9.2.3 Improving User Authentication
9.2.4 Enhancing User Experience and Trust
9.2.5 Enabling Proactive Security Measures
9.3 Behavioral Biometrics Enhanced by Generative AI
9.3.1 Introduction to Behavioral Biometrics
9.3.2 Fundamental Principles of Behavioral Biometrics
9.3.3 Integrating Generative AI with Behavioral Biometrics
9.3.4 Enhancing Accuracy and Reliability
9.4 Formulating User-Centric Security Policies
9.4.1 Challenges in Policy Formulation
9.4.2 AI’s Role in Policy Adaptation and Implementation
9.4.3 Ethical Considerations and User Privacy
9.5 Human-AI Collaboration in Security Frameworks
9.5.1 Key Components of Human-AI Collaboration
9.5.2 Models of Human-AI Interaction
9.5.3 Experimental Workflow and Findings
9.6 Future Trends in Collaborative Security
9.7 Challenges and Future Directions
9.7.1 Technical Challenges
9.7.2 Anticipating Future Threat Landscapes
9.7.3 Human-AI Collaborative Defense
9.8 Conclusion
References
10. Human Centric Security: Human Behavior Analysis Based on GenAI
P. Muralidhar, Ch. Raja Ramesh, V. K. S. K. Sai Vadapalli and Bh. Lakshmi Madhuri
10.1 Introduction
10.2 Model of ChatGPT
10.3 Human Interaction with ChatGPT
10.4 Impact of GAI in Cyber Security
10.5 Attacks Enhanced by GAI
10.6 Replicate Version of ChatGPT
10.6.1 Vulnerabilities of GAI Models
10.6.2 Road Map of GAI in Cybersecurity and Privacy
10.7 Enhancement of Destructions with ChatGPT
10.8 Protection Measures Using GAI Models
10.8.1 Cyber Security Reporting
10.8.2 Generating Secure Code Using ChatGPT
10.8.3 Detection the Cyber Attacks
10.8.4 Improving Ethical Guidelines
10.9 GAI Tools to Boost Security
10.10 Future Trends and Challenges
10.11 Conclusion
References
11. Machine Learning-Based Malicious Web Page Detection Using Generative AI
Ashwini Kumar, Harikesh Singh, Mayank Singh and Vimal Gupta
11.1 Introduction
11.1.1 Background and Motivation
11.1.2 Threat Landscape: Rise of Malicious Web Pages
11.1.3 Role of ML and GenAI in Cybersecurity
11.1.4 Objectives of the Chapter
11.2 Related Work
11.2.1 Signature-Based Detection Systems
11.2.2 Heuristic and Rule-Based Techniques
11.2.3 Traditional ML Approaches: SVM, Decision Trees, Random Forests
11.2.4 Deep Learning for Web Page Classification
11.2.5 Recent Advances in GenAI for Cybersecurity
11.2.6 Comparative Analysis of Approaches
11.3 Methodology
11.3.1 Data Collection and Preprocessing
11.3.2 Feature Engineering
11.3.3 Machine Learning Models
11.3.4 Integrating Generative AI
11.3.5 Hybrid Detection Architecture
11.4 Experimental Evaluation
11.4.1 Datasets
11.4.2 Preprocessing and Feature Extraction
11.4.3 Experimental Setup
11.4.4 Evaluation Metrics
11.4.5 Results
11.5 Challenges and Limitations
11.5.1 Evasion Techniques and Obfuscation
11.5.2 Data Quality and Labeling
11.5.3 Generalization and Domain Adaptation
11.5.4 Dual-Use Nature of Generative AI
11.5.5 Explainability and Interpretability
11.6 Conclusion
11.7 Future Directions
11.7.1 Adaptive and Continual Learning
11.7.2 Multi-Modal Threat Analysis
11.7.3 Explainable AI (XAI) in Detection Pipelines
11.7.4 Federated and Privacy-Preserving Learning
11.7.5 Responsible Use of Generative AI
References
12. A Comprehensive Survey of the 6G Network Technologies: Challenges, Possible Attacks, and Future Research
Riddhi V. Harsora, Sushil Kumar Singh, Ravikumar R. N., Deepak Kumar Verma and Santosh Kumar Srivastava
12.1 Introduction
12.2 Related Work
12.2.1 6G Necessities
12.2.1.1 Virtualization Security Solution
12.2.1.2 Automated Management System
12.2.1.3 Users’ Privacy-Preservation
12.2.1.4 Data Security Using AI
12.2.1.5 Post-Quantum Cryptography
12.2.1.6 Security Issues and Solutions
12.2.1.7 Low-Latency Communication
12.2.1.8 Terahertz Communication
12.2.1.9 Quantum-Safe Encryption
12.2.1.10 Privacy-Preserving Techniques
12.2.1.11 Reliability and Resilience
12.2.1.12 Authentication and Authorization
12.2.1.13 AI-Driven Network Optimization
12.2.1.14 Malware and Cyber Attacks
12.3 6G Security: Possible Attacks and Solutions on Emerging Technologies
12.3.1 Physical Layer Security
12.3.1.1 Visible Light Communication Technology
12.3.1.2 Terahertz Technology
12.3.1.3 Molecular Communication
12.3.2 ABC Security
12.3.2.1 Artificial Intelligence
12.3.2.2 Blockchain
12.3.2.3 Quantum Communication
12.4 6G Survey Scenario and Future Scope
12.4.1 6G Survey Scenario
12.4.2 6G Future Scope
12.5 Conclusion
Acknowledgment
References
13. RDE-GAI-IDS: Real-Time Distributed Ensemble and Generative-AI-Based Intrusion Detection System to Detect Threats in Edge Computing Networks
Amit Kumar, Vivek Kumar, Manoj Kumar Mahto and Abhay Pratap Singh Bhadauria
13.1 Introduction
13.2 Related Work
13.3 Proposed Methodology
13.3.1 Dataset Description
13.3.2 Data Integration
13.3.3 Data Pre-Processing (DP)
13.3.4 Remove Missing and Infinite Feature Values
13.3.5 Data Normalization
13.3.6 Feature Selection
13.3.7 Generative Artificial Intelligence (GAI)
13.4 Constructing the Model
13.4.1 RF Algorithm
13.4.2 DT Algorithm
13.4.3 ET Algorithm
13.4.4 KNN Algorithm
13.4.5 Training and Testing
13.5 Experimental Results & Discussion
13.5.1 Performance Evaluation Criteria
13.5.2 Comparison with Previous Methods
13.6 Conclusion
References
14. Leveraging Generative AI for Advanced Threat Detection in Cybersecurity
Anuradha Reddy, Mamatha Kurra, G. S. Pradeep Ghantasala and Pellakuri Vidyullatha
14.1 Introduction
14.2 Purpose
14.3 Scope
14.4 History
14.5 Applications in Industry
14.6 Applications in Defense
14.6.1 Leveraging Generative AI for Advanced Threat Detection in Cybersecurity in Banking
14.6.2 Leveraging Generative AI for Advanced Threat Detection in Cybersecurity in Military Applications
14.6.3 Leveraging Generative AI for Advanced Threat Detection in Cybersecurity in Health Care Applications
14.7 Challenges and Considerations
14.7.1 Future Trends and Directions
14.8 Conclusion
References
15. Quantum Computing and Generative AI-Securing the Future of Information
Deeya Shalya, Rimon Ranjit Das and Gurpreet Kaur
15.1 Introduction
15.2 Generative AI-Enabled Intelligent Resource Allocation for Quantum Computing Networks
15.3 The Synergy of Two Worlds: Bridging Classical and Quantum Computing in Hybrid Quantum-Classical Machine Learning Models
15.3.1 The Collaborative Approach
15.3.2 Real-World Application
15.4 Generative AI in Medical Practice: Privacy and Security Challenges
15.4.1 Introduction
15.5 Quantum Machine Learning
15.5.1 Background
15.5.2 Complexity
15.6 qGAN-Quantum Generative Adversarial Network
15.6.1 Linear-Algebra Based Quantum Machine Learning
15.6.1.1 Quantum Principal Component Analysis
15.6.1.2 Quantum Support Vector Machines and Kernel Methods
15.6.1.3 qBLAS Based Optimization
15.6.1.4 NT Angled Datasets for Quantum Machine Learning
15.6.2 Reading Classical Data into Quantum Machines
15.6.3 Deep Quantum Learning
15.6.4 Quantum Machine Learning for Quantum Data
15.7 The Impact of the NISQ Era on Quantum Computing and Generative AI
15.7.1 Quantum Machine Learning in the NISQ Era
15.7.2 Quantum Convolution Neural Network
15.8 Conclusion and Future Scope
15.8.1 Challenges in Resource Allocation for Quantum Computing Networks
15.8.2 Barren Plateaus
Acknowledgements
References
16. Redefining Security: Significance of Generative AI and Difficulties of Conventional Encryption
R. Nandhini, Gaurab Mudbhari and S. Prince Sahaya Brighty
16.1 Introduction
16.1.1 Encryption’s Significance in Cybersecurity
16.2 Traditional Encryption Techniques
16.2.1 Different Encryption Method Types
16.2.1.1 Symmetric Encryption
16.2.1.2 Asymmetric Encryption
16.2.1.3 Hash Functions
16.2.2 Challenges and Limitations of Conventional Encryption
16.2.2.1 Brute-Force Attacks
16.2.2.2 Issue in Key Management
16.2.2.3 Blind Spots in Anomaly Detection
16.3 Introduction to Generative AI
16.3.1 Unimodal (CV & NLP)
16.3.2 Combining Different Modes—Visual and Linguistic
16.3.3 The Potential of Generative AI for Data Simulation
16.3.3.1 Beneficial Patterns in the Data
16.3.3.2 User Behavior Modeling
16.4 Applications of Generative AI in Cybersecurity
16.4.1 Deceptive Honeypots
16.4.2 Dynamic Defense Systems
16.4.3 An Application of Generative AI in E-Commerce Platforms and to Update Its Adaptive Data Systems
16.4.4 Adaptive Data System Updates
16.4.5 Predictive Threat Identification
16.4.6 Behavioral Biometrics for Anomaly Detection
16.4.7 Enhanced User Authentication Systems
16.5 Problems in Implementing Generative AI
16.5.1 Algorithm Fairness and Bias
16.5.2 Ensuring Equitable AI Decisions
16.5.3 Taking on Malevolent AI Models
16.5.4 Technical Resource Demands for Generative AI
16.6 Combining Generative AI with Traditional Methods
16.6.1 Hybrid Security Models
16.7 Emerging Trends in AI and Security: A Double-Edged Sword
16.7.1 AI-Powered Attacks
16.7.1.1 AI in Defense: Strengthening the Cybersecurity Barrier
16.7.1.2 Explainable AI (XAI): Establishing Transparency and Trust
16.7.1.3 Generative AI: A Powerful Tool with Potential Risks
16.8 Conclusion
References
Index

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