Call for Chapters: Machine Intelligence Applications in Cyber-Risk Management

Editors

Mohammed Almaiah, The University of Jordan, Jordan
Yassine Maleh, Sultan Moulay Slimane University, Morocco

Call for Chapters

Proposals Submission Deadline: July 7, 2024
Full Chapters Due: August 18, 2024
Submission Date: August 18, 2024

Introduction

In an era where cyber threats are increasingly sophisticated and persistent, the intersection of machine intelligence and cyber-risk management represents a pivotal frontier in the defense against malicious actors. The rapid advancements in artificial intelligence (AI) and machine learning (ML) technologies offer unprecedented capabilities for identifying, analyzing, and mitigating cyber risks. This book, Machine Intelligence Applications in Cyber-Risk Management, edited by Mohammed Almaiah and Yassine Maleh, delves into the transformative impact of these technologies in safeguarding organizations against an evolving landscape of cybersecurity threats. The digital transformation of industries has led to an exponential increase in the complexity and volume of cyber threats. Traditional cybersecurity measures, while still important, are often inadequate in the face of advanced and adaptive cyber-attacks. Here, machine intelligence emerges as a game-changer. By leveraging AI and ML, organizations can enhance their cyber defenses with real-time threat detection, anomaly detection, predictive analytics, and behavioral analysis. These technologies not only improve the speed and accuracy of identifying potential threats but also enable proactive and adaptive security measures. This book explores the diverse applications of machine intelligence in cyber-risk management, providing a comprehensive overview of how AI and ML algorithms are utilized for automated incident response, threat intelligence gathering, and dynamic security postures. It addresses the pressing need for innovative solutions to combat cyber threats and offers insights into the future of cybersecurity, where machine intelligence plays a crucial role in creating resilient and adaptive defense mechanisms. The chapters within this book bring together cutting-edge research and practical implementations from leading experts in the field. They cover a wide range of topics, including but not limited to, the design and deployment of AI-driven cybersecurity systems, the integration of ML algorithms in existing security infrastructures, and case studies showcasing successful applications of machine intelligence in mitigating real-world cyber threats. Through this comprehensive exploration, Machine Intelligence Applications in Cyber-Risk Management aims to equip cybersecurity professionals, researchers, and decision-makers with the knowledge and tools needed to stay ahead of emerging cyber threats. By understanding and harnessing the power of machine intelligence, organizations can fortify their defenses and ensure a secure digital future. We invite researchers, practitioners, and experts in the field of cybersecurity and machine intelligence to contribute to this essential compilation of knowledge. Your insights and innovations will help shape the future of cyber-risk management and advance our collective ability to protect against the ever-evolving threat landscape.

Objective

Machine Intelligence Applications in Cyber-Risk Management aims to explore the transformative impact of artificial intelligence (AI) and machine learning (ML) on cybersecurity. The book will illustrate how these technologies enhance traditional cybersecurity measures, offering advanced capabilities such as real-time threat detection, anomaly detection, predictive analytics, and behavioral analysis. By presenting diverse applications, including automated incident response and adaptive security measures, the book seeks to demonstrate the potential and versatility of AI and ML in managing cyber risks. This book intends to bridge the gap between theoretical research and practical implementation by including case studies, real-world examples, and insights from industry experts. It aims to inspire further research and innovation in the field, providing cybersecurity professionals, researchers, and decision-makers with the knowledge and tools needed to combat cyber threats. By fostering collaboration and knowledge sharing among experts, the book will contribute to the advancement of cyber-risk management and the development of innovative solutions to protect against evolving cyber threats.

Target Audience

The primary audience for Machine Intelligence Applications in Cyber-Risk Management includes cybersecurity professionals, researchers, and academic scholars who are involved in the field of cybersecurity and are seeking to enhance their understanding of the latest advancements in artificial intelligence (AI) and machine learning (ML) applications. This book will be particularly valuable to security analysts, threat intelligence experts, and incident response teams who are on the front lines of defending against cyber threats and are looking for innovative approaches to improve their strategies and tools. Additionally, the book is geared towards IT managers, decision-makers, and policy-makers in organizations who are responsible for implementing and overseeing cybersecurity initiatives. These individuals will benefit from the practical insights and case studies that demonstrate how AI and ML can be effectively integrated into existing security frameworks to mitigate risks and enhance overall security posture. Students and educators in cybersecurity and computer science programs will also find this book to be an essential resource for understanding the intersection of machine intelligence and cyber-risk management, providing a solid foundation for future research and professional development in this rapidly evolving field.

Recommended Topics

• Real-time Threat Detection using AI and ML • Anomaly Detection Algorithms in Cybersecurity • Predictive Analytics for Cyber Risk Management • Behavioral Analysis for Identifying Cyber Threats • Automated Incident Response Systems • Threat Intelligence Gathering with Machine Intelligence • Adaptive Security Measures and AI • Integration of AI and ML in Existing Security Infrastructures • Case Studies of AI and ML Applications in Cybersecurity • Design and Deployment of AI-Driven Cybersecurity Systems • Machine Learning Techniques for Network Security • AI-Based Solutions for Malware Detection and Prevention • Use of Deep Learning in Cyber Risk Analysis • Natural Language Processing (NLP) for Threat Intelligence • AI-Enhanced Vulnerability Assessment and Management • Predictive Maintenance of Cybersecurity Systems using ML • Ethical and Legal Implications of AI in Cybersecurity • Future Trends and Innovations in Machine Intelligence for Cyber Risk Management • Comparative Analysis of AI and Traditional Cybersecurity Approaches • Challenges and Limitations of Implementing AI in Cyber Risk Management

Submission Procedure

Researchers and practitioners are invited to submit on or before July 7, 2024, 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 July 10, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by August 18, 2024, 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, Machine Intelligence Applications in Cyber-Risk Management. 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 (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 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 2025.



Important Dates

July 7, 2024: Proposal Submission Deadline
July 10, 2024: Notification of Acceptance
August 18, 2024: Full Chapter Submission
September 22, 2024: Review Results Returned
October 20, 2024: Final Acceptance Notification
October 27, 2024: Final Chapter Submission



Inquiries

Dr. Mohammed Almaiah
The University of Jordan
m.almaiah@ju.edu.jo

Dr. Yassine Maleh
Sultan Moulay Slimane University
yassine.maleh@ieee.org



Classifications


Computer Science and Information Technology; Security and Forensics; Physical Sciences and Engineering
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