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CyberS_DL 2022 : Intersecting Privacy, Deep Learning, and Reinforcement Learning in Cybersecurity

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Link: https://www.igi-global.com/publish/call-for-papers/call-details/5477
 
When Oct 1, 2021 - Apr 1, 2022
Where Book Chapter
Submission Deadline Nov 24, 2021
Notification Due Jan 14, 2022
Final Version Due Feb 21, 2022
Categories    deep learning   cyber security   machine learning   privacy
 

Call For Papers

Call for Book Chapter
Intersecting Privacy, Deep Learning, and Reinforcement Learning in Cybersecurity
To be published by IGI Global Publisher

A massive number of techniques have been proposed to ensure authenticity, integrity, and confidentiality of data towards secure applications, e.g., network intrusion detection, biometrics. Deep learning techniques are superior to the performance achieved by classical models-based and traditional machine learning approaches. The main aim of this book is to propose advanced deep learning security tools and investigate the strength, challenges of deep learning techniques for security-oriented applications.
This book aims to provide a comprehensive reference for security, privacy, deep learning, and reinforcement learning by covering topics of recent trends. It will be divided into sections and with each section, extends the growing literature on the emerging technologies and innovations would be covered. Thus, it would not only serve as a reference for existing cybersecurity, privacy and deep learning, but it would also provide future directions in these topics. This editing book has the dual objective of encouraging more research in security, privacy, deep learning and reinforcement learning and at the same time of publishing the best research being conducted today. In the past 5 years, there is no an edited book that focuses in merging the security, privacy and deep learning in cyber-physical systems and IoT platforms. There are few edited books that focus on each perspective individually. However, merging both perspectives give a clear view about the current and future development.
This book aims to provide a comprehensive reference for blockchain and deep learning by covering all important topics. It encourages recent studies of blockchain, deep learning and reinforcement learning with focusing on the following topics but not limited to:
Topics:
• Deep Learning Solutions Against Recent Cyber Threats
• Privacy preserving applications using deep learning models
• Intrusion detection by Deep Learning
• Cyber-Physical Surveillance systems based on deep learning
• Cyberthreat intelligence and classification
• Secure deep learning algorithms
• Deep learning Cybersecurity novel applications
• Cybersecurity and Machine Learning/Artificial Intelligence
• Security for IoT-driven intelligence and incorporate deep learning models
• Deep Learning and Cybersecurity in connected and autonomous vehicles
• Secure online inferencing
• Reinforcement learning
• Deep learning models for achieving safety

This book will be published by IGI Global publisher and submitted for indexing to Web of Science, Scopus, Inspec, dblp, and Ei Compendex.

Submission Procedure
All book chapters proposal must be electronically submitted by using eEditorial Discovery®TM, following these guidelines:
• Researchers and practitioners are kindly invited to submit chapter proposal
containing a preliminary title, a short abstract and authors affiliations.
• The length of the full book chapter should be between 15 to 20 pages (including reference).
• All submitted chapters will be reviewed by at least three reviewers on a double-blind review basis
• Submission link: https://www.igi-global.com/publish/call-for-papers/call-details/5477

Important Dates:


Nov. 24, 2021: Full chapter submission deadline
Jan. 18, 2022: Acceptance/rejection notification to authors
Feb. 1, 2022: Final Chapter Submission (camera ready version)


Editors:

Khaled R Ahmed
School of Computing, Southern Illinois University, USA
kahmed@cs.siu.edu

Abdullah Aydeger
School of Computing, Southern Illinois University, USA
aydeger@cs.siu.edu
AboulElla Hassanien
Cairo University, Faculty of Computer and Information Egypt
aboitcairo@fci-cu.edu.eg

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