AI-Sec 2021: Cyber Security and Adversarial Machine Learning: Emerging Attacks and Mitigation Strategies |
Website | https://www.igi-global.com/publish/call-for-papers/call-details/5243 |
Submission link | https://easychair.org/conferences/?conf=aisec2021 |
Introduction
Machine Learning is making our daily lives as digital as possible, and this new era is called Artificial Intelligence. The binding force behind the rapid growth of machine learning (or deep learning) is enterprises' technological advances. In recent years, machine learning algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous car, and many more. With the rapid developments of deep learning applications, it is crucial to understand the security concern into account when implementing the models. While deep learning applications allow notable benefits for enterprise applications, AI models' security is disregarded by the developer community so far. However, security is also an essential part of the AI models because attackers can manipulate the AI model itself.
Objective
In this context, this book will address the cybersecurity and security of the AI application challenges connected with the enterprises, which will provide a bigger picture of the theories, intelligent methods, methods, and open research directions in this domain. Furthermore, the proposed book will assist as a single source of reference for acquiring knowledge on the technology, process involved in the next-generation cybersecurity.
Target Audience
This book intends to bring collectively the analyses and insights of researchers and scientists worldwide, but practitioners are also more than welcomed as chapter authors. All types of contributions are considered, ranging from real-life case studies to best practices, conceptual papers, empirical studies, literature reviews, and the like. This book aims to analyze AI and cybersecurity from a holistic perspective and provide a balanced and critical account of the sector's digitalization, opportunities, impact and challenges and showcase a wide variety of opinions and viewpoints.
Recommended Topics
- Foundations of understanding adversarial machine learning
- Theory and algorithms for attacking with adversarial learning
- Theory and algorithms of defending adversarial attacks
- Novel applications of adversarial learning and security
- Business data security with adversarial training
- Medical/health informatics with security
- Biological data analysis with security
- Biometric recognition with security and privacy
- Explainable machine learning for cyberspace security and safety
- Human-machine intelligence for cyberspace security and safety
- Cloud security and AI - Secure AI modelling and architecture
- The novel cryptographic mechanism for AI
- Cyberspace security and safety for 5G/6G
- Cyberspace security and safety for industry 4.0/5.0
Important Dates
- May 12, 2021: Proposal Submission Deadline
- May 26, 2021: Notification of Acceptance
- July 25, 2021: Full Chapter Submission
- September 7, 2021: Review Results Returned
- October 19, 2021: Final Acceptance Notification
- November 2, 2021: Final Chapter Submission
Inquiries
Ferhat Ozgur Catak
Simula Research Lab.
ozgur@simula.no - f.ozgur.catak@gmail.com