Overview
The call for chapters invites contributions for a forthcoming book titled “Adversary Aware Learning Techniques and Trends in Cybersecurity,” to be published by Springer as part of their Artificial Intelligence series. This book aims to address the urgent need for enhancing the robustness of machine learning systems against adversarial attacks, a growing concern in today’s technology landscape.
Background & Relevance
Machine learning technologies have become integral to various applications, from smart home devices to automated decision-making systems. However, as these systems proliferate, their susceptibility to adversarial attacks poses significant risks. Research focusing on the security, trust, and reliability of these systems is crucial for ensuring their safe deployment in real-world scenarios. This book seeks to compile insights and advancements in adversarial machine learning, particularly in the context of cybersecurity, to help mitigate these vulnerabilities.
Key Details
- Submission Deadline: October 15, 2019
- Review Notifications: December 15, 2019
- Revised Manuscripts Due: December 31, 2019
- Final Accept/Reject Decisions: January 15, 2020
- Final Manuscripts Due: January 31, 2020
- Publication Date: Second quarter of 2020
- Submission Link: EasyChair
- Editors: Prithviraj Dasgupta, Joseph Collins, Ranjeev Mittu
- Contact Email: prithviraj.dasgupta@nrl.navy.mil
Eligibility & Participation
This call for chapters is open to researchers, practitioners, and academics interested in contributing to the field of adversarial machine learning and its implications for cybersecurity. Contributions should focus on current trends, challenges, and solutions in the domain.
Submission or Application Guidelines
- Manuscripts must adhere to the Springer style guidelines available on the book’s website.
- The maximum page length for submissions is 20 pages, including references.
- Submissions should be in PDF format and uploaded via EasyChair.
Additional Context / Real-World Relevance
As machine learning systems become more embedded in critical infrastructure and everyday technology, ensuring their resilience against adversarial threats is paramount. This book will serve as a valuable resource for understanding the intersection of machine learning and cybersecurity, providing insights into how to build more secure systems.
Conclusion
This is an excellent opportunity for those in the field of machine learning and cybersecurity to share their research and insights. Interested contributors are encouraged to submit their chapters and help advance the discourse on adversarial machine learning in cybersecurity.
Category: CFP & Deadlines
Tags: adversarial machine learning, cybersecurity, deep learning, reinforcement learning, generative adversarial networks, network intrusion detection, malware detection, human factors, game theory
