Hershey, Pennsylvania
New York, New YorkBeijing, China

Call for Chapters: Advancing Cyber Threat Detection Through Quantum and Edge Computing

Editors

SHENSON JOSEPH, University of North Dakota, United States
Kishor Kumar Reddy C, Stanley College of Engineering & Technology for Women, India
Asegul Hulus, University of Greenwich, Cyprus
Tatjana Sibalija, Union University, Serbia

Call for Chapters

Proposals Submission Deadline: April 27, 2025
Full Chapters Due: June 29, 2025
Submission Date: June 29, 2025

Introduction

"Advancing Cyber Threat Detection Through Quantum and Edge Computing" is your ultimate guide to the future of AI-driven cybersecurity and predictive intelligence. This book unpacks how Quantum Computing and AI are revolutionizing fraud detection, cyber defense, and real-time anomaly detection across industries. With cutting-edge research, real-world applications, and expert insights, this book will equip you with the knowledge and strategies to stay ahead in the next wave of technological disruption. Whether you're a data scientist, cybersecurity professional, or tech innovator, this book provides a roadmap to harness Quantum AI for unparalleled security and efficiency. Get ready to unlock the future—because the next frontier of AI is quantum-powered!

Objective

In an era where cyber threats, financial fraud, and data breaches are evolving at an unprecedented pace, traditional security measures struggle to keep up. "Advancing Cyber Threat Detection Through Quantum and Edge Computing" explores how Quantum Computing and Artificial Intelligence (AI) are reshaping the landscape of real-time anomaly detection, predictive analytics, and next-gen cybersecurity. This book provides a deep dive into how quantum-enhanced AI models can detect patterns, adapt to emerging threats, and revolutionize security frameworks across industries, from finance and healthcare to national security and cloud infrastructure. This book begins by demystifying Quantum Computing and its role in AI, explaining how quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNNs), and Quantum Boltzmann Machines (QBMs) can outperform classical models in anomaly detection. Readers will gain a foundational understanding of how quantum-powered AI models enhance fraud detection, risk mitigation, and cyber threat intelligence, with real-world case studies from financial markets, telecommunications, and IoT security. Next, the book delves into practical implementations, guiding readers through building AI-driven anomaly detection frameworks with Quantum Machine Learning (QML). Step-by-step technical insights, code samples, and workflow blueprints will help data scientists, engineers, and cybersecurity professionals develop highly efficient, scalable, and adaptive detection systems. Whether tackling deepfake detection, insider threats, or high-frequency trading fraud, readers will discover how to integrate quantum-enhanced AI solutions into modern risk management systems. Finally, the book explores the future of Quantum AI in anomaly detection, covering emerging trends, regulatory challenges, and ethical considerations. As Quantum AI continues to evolve, its impact on data privacy, adversarial AI, and autonomous security systems will be profound. This book equips readers with the knowledge and tools to stay ahead of the quantum revolution, ensuring that organizations are prepared for the next frontier of AI-driven security and risk intelligence.

Target Audience

1. AI & Machine Learning Experts • Machine Learning Engineers – Implementing AI-powered anomaly detection in large-scale systems. • Data Scientists – Developing predictive models for fraud detection, cybersecurity, and financial risk analysis. • Deep Learning Researchers – Exploring Quantum AI applications in anomaly detection and pattern recognition. 2. Cybersecurity & Risk Management Professionals • Cybersecurity Analysts – Detecting and mitigating cyber threats, fraud, and network intrusions using AI. • Security Operations Center (SOC) Managers – Managing real-time anomaly detection systems for threat intelligence. • Fraud Detection Specialists – Implementing quantum-enhanced AI for fraud prevention in financial services and e-commerce. 3. Quantum Computing & Theoretical AI Researchers • Quantum Computing Scientists – Exploring Quantum Machine Learning (QML) techniques for anomaly detection. • AI and Quantum Research Fellows – Investigating how quantum-enhanced AI models outperform classical detection systems. 4. Business & Technology Executives • Chief Information Security Officers (CISOs) – Seeking next-generation AI solutions for enterprise security. • Chief Technology Officers (CTOs) & AI Strategists – Exploring Quantum AI’s impact on industry transformation. • FinTech Leaders – Leveraging AI-powered fraud detection in banking, insurance, and digital transactions. 5. Government & Defense Professionals • Intelligence Analysts & Cyber Defense Experts – Applying Quantum AI for national security and cybersecurity resilience. • Regulatory Compliance Officers – Understanding the implications of Quantum AI in risk management and data security.

Recommended Topics

Tentative Chapters (Suggestions are Welcome) 1. The Rise of AI-Powered Anomaly Detection 2. Quantum Computing: The Basics for AI Practitioners 3. Introduction to Quantum Machine Learning (QML) 4. Why Traditional AI Fails in Advanced Anomaly Detection 5. Quantum Neural Networks (QNNs) for Anomaly Detection 6. Quantum Support Vector Machines (QSVMs) for Pattern Recognition 7. Variational Quantum Circuits (VQCs) for Adaptive AI 8. Generative AI and Quantum Computing: The Perfect Pair 9. AI-Powered Fraud Detection in Financial Markets 10. Quantum AI for Cybersecurity and Threat Intelligence 11. Anomaly Detection in Healthcare and Life Sciences 12. Quantum Computing in Cloud Security and IoT Networks 13. The Future of Quantum AI in Security and Risk Management 14. Ethical Challenges and Regulatory Considerations 15. Building a Quantum AI Strategy: Roadmap for Businesses and Professionals

Submission Procedure

Researchers and practitioners are invited to submit on or before April 27, 2025, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by May 11, 2025 about the status of their proposals and sent chapter guidelines.Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by June 29, 2025, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Advancing Cyber Threat Detection Through Quantum and Edge Computing. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global Scientific Publishing (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global Scientific Publishing specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2026.



Important Dates

April 27, 2025: Proposal Submission Deadline
May 11, 2025: Notification of Acceptance
June 29, 2025: Full Chapter Submission
August 3, 2025: Review Results Returned
August 31, 2025: Final Acceptance Notification
September 7, 2025: Final Chapter Submission



Inquiries

SHENSON JOSEPH University of North Dakota shenson.joseph@gmail.com Kishor Kumar Reddy C Stanley College of Engineering & Technology for Women drckkreddy@gmail.com Asegul Hulus University of Greenwich/ ACM-W asegulhulus@outlook.com Tatjana Sibalija Union University tsibalija@gmail.com

Classifications


Computer Science and Information Technology
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