Call for Chapters: Application of Large Language Models (LLMs) for Software Vulnerability Detection

Editors

Marwan Omar, Illinois Institute of Technology, United States
Hewa Zangana, Dahok Politechnical University, Iraq

Call for Chapters

Proposals Submission Deadline: September 29, 2024
Full Chapters Due: December 1, 2024
Submission Date: December 1, 2024

Introduction

The rapid evolution of software systems and the increasing complexity of their architectures have led to an alarming rise in software vulnerabilities. These vulnerabilities pose significant risks to the security, integrity, and reliability of software systems, making their detection and mitigation a critical priority for both researchers and practitioners. Traditional methods of vulnerability detection, while effective to some extent, often struggle to keep pace with the growing sophistication of cyber threats. In recent years, Large Language Models (LLMs) have emerged as powerful tools for a wide range of natural language processing tasks. Leveraging their ability to understand and generate human-like text, LLMs have shown promise in various domains, including software engineering. By analyzing vast amounts of code and related documentation, LLMs can identify patterns, anomalies, and potential vulnerabilities that might be missed by conventional static and dynamic analysis tools. This book aims to explore the application of LLMs in the detection of software vulnerabilities, highlighting their potential to revolutionize the field. By bringing together cutting-edge research and practical case studies, this volume will provide readers with a comprehensive understanding of how LLMs can be harnessed to enhance software security, mitigate risks, and ultimately contribute to the development of more resilient software systems.

Objective

The primary objective of this book is to explore and elucidate the application of Large Language Models (LLMs) for software vulnerability detection, offering a deep dive into how these advanced models can be leveraged to enhance software security. As software systems continue to grow in complexity, the need for innovative and efficient methods of identifying vulnerabilities becomes increasingly critical. This book seeks to bridge the gap between traditional vulnerability detection methods and the emerging capabilities of LLMs, providing a comprehensive resource for both researchers and practitioners. By bringing together contributions from leading experts in the field, the book aims to: Advance Research in Software Security: The book will contribute to the ongoing research by presenting novel methodologies, frameworks, and case studies that demonstrate the practical application of LLMs in detecting software vulnerabilities. It will address current challenges and provide insights into how LLMs can be integrated into existing security practices. Promote Interdisciplinary Collaboration: By combining insights from the fields of natural language processing, machine learning, cybersecurity, and software engineering, the book will foster interdisciplinary collaboration, encouraging the exchange of ideas and the development of holistic solutions to software security challenges. Provide Practical Guidance: In addition to theoretical contributions, the book will offer practical guidance for implementing LLM-based vulnerability detection in real-world software development and security workflows. It will include best practices, tools, and techniques that practitioners can apply to improve the security of their systems. Identify Future Research Directions: The book will not only document current advancements but also identify gaps in the existing research, offering a roadmap for future studies. It will highlight emerging trends, potential applications, and the challenges that need to be addressed to fully realize the potential of LLMs in software security. By achieving these objectives, the book will serve as a foundational text in the field, providing a rich source of knowledge that will help to shape the future of software vulnerability detection and contribute to the development of more secure and robust software systems.

Target Audience

This book is primarily geared towards a diverse audience of professionals, academics, and students who are engaged in the fields of software security, machine learning, and software engineering. The book’s interdisciplinary approach ensures that it will be valuable to a wide range of readers, including: Cybersecurity Professionals: Security analysts, ethical hackers, and incident responders will benefit from understanding how Large Language Models (LLMs) can be applied to detect software vulnerabilities more effectively. The book will provide them with new tools and methodologies to enhance their existing security practices. Software Developers and Engineers: Software developers and engineers who are responsible for designing and maintaining secure software systems will find practical guidance on integrating LLM-based vulnerability detection into their development workflows. The book will help them understand how to identify and mitigate vulnerabilities during the coding process. Machine Learning Researchers and Practitioners: For those in the field of machine learning and artificial intelligence, the book offers insights into the application of LLMs beyond traditional NLP tasks. Researchers and practitioners will discover new avenues for applying their expertise in the domain of software security. Academics and Students: Graduate students, researchers, and educators in computer science, cybersecurity, and software engineering will find the book an essential resource for understanding the latest developments at the intersection of LLMs and software security. It will also serve as a reference text for courses and research projects related to software vulnerability detection. Industry Leaders and Decision-Makers: Executives, managers, and policy-makers in technology companies, especially those involved in software development and cybersecurity, will benefit from the strategic insights provided by the book. It will help them make informed decisions about adopting LLM-based security measures and staying ahead of emerging threats. Security Tool Developers: Developers of security tools and platforms will gain an understanding of how LLMs can be integrated into their products to provide enhanced vulnerability detection capabilities, potentially leading to the development of new, cutting-edge tools. By addressing the needs of these varied audiences, the book will not only disseminate cutting-edge research but also have a practical impact on the development and deployment of more secure software systems.

Recommended Topics

Introduction to Large Language Models (LLMs): Overview of LLMs and their evolution Core principles and architectures of LLMs Applications of LLMs in various domains Software Vulnerability Detection: Traditional methods of software vulnerability detection Challenges in detecting software vulnerabilities The role of machine learning in enhancing vulnerability detection LLMs in Software Security: Leveraging LLMs for static and dynamic code analysis LLM-based techniques for identifying code smells and vulnerabilities Automated code review and security auditing using LLMs Integration of LLMs with Existing Security Tools: Combining LLMs with static analysis tools Enhancing dynamic analysis with LLM-driven insights Case studies on integrating LLMs with continuous integration/continuous deployment (CI/CD) pipelines LLM-Based Vulnerability Prediction: Predicting potential vulnerabilities during software development Early detection of security flaws in software design Using LLMs to prioritize vulnerabilities based on risk assessment Case Studies and Practical Implementations: Real-world applications of LLMs in software security Success stories and lessons learned from implementing LLM-based vulnerability detection Case studies on mitigating security incidents using LLMs Challenges and Limitations of Using LLMs for Security: Addressing the limitations of LLMs in vulnerability detection Ethical and privacy concerns in the use of LLMs for security purposes Overcoming adversarial attacks on LLM-based security systems Future Directions and Emerging Trends: The evolving role of LLMs in software security Potential advancements in LLM architectures for better security outcomes Emerging trends in AI-driven software vulnerability detection Interdisciplinary Approaches: Combining LLMs with other AI techniques for robust security solutions Collaborative approaches involving security experts and AI researchers Cross-domain applications of LLMs in cybersecurity Tools and Frameworks for LLM-Based Vulnerability Detection: Overview of tools and platforms that support LLM-based analysis Open-source frameworks and libraries for implementing LLMs in security Guidelines for building custom LLM-based security tools This list of topics is intended to provide a comprehensive overview of the potential areas that the book will cover, offering insights and practical guidance across multiple dimensions of LLM-based software vulnerability detection.

Submission Procedure

Researchers and practitioners are invited to submit on or before September 29, 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 October 13, 2024 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 December 1, 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, Application of Large Language Models (LLMs) for Software Vulnerability Detection. 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

September 29, 2024: Proposal Submission Deadline
October 13, 2024: Notification of Acceptance
December 1, 2024: Full Chapter Submission
January 5, 2025: Review Results Returned
February 2, 2025: Final Acceptance Notification
February 17, 2025: Final Chapter Submission



Inquiries

Marwan Omar
Illinois Institute of Technology
drmarwan.omar@gmail.com

Hewa Majeed Zangana
Duhok Polytechnic University
hewa.zangana1987@gmail.com



Classifications


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