Special Session on Cybersecurity Issues of IoT in Ambient Intelligence (AmI) Environment (2nd Edition)
Track ID: Spes-02
Over the years, the use of the Internet of Things (IoT) has come to dominate several areas, e.g., improving our lives, offering us convenience, and reshaping our daily work circumstances. Ambient intelligence (AmI) refers to the ability of devices to interact seamlessly with their surroundings. The increased use of IoT in ambient intelligence has led to a heightened concern for cybersecurity. Hackers could exploit vulnerabilities in the software or firmware of IoT devices to gain control of the devices or the networks they are connected to. They could also use ambient intelligence systems to collect sensitive data from IoT devices. In order to protect these devices, it’s essential to understand the various types of attacks that are possible and deploy appropriate security measures. In recent years, Artificial Intelligence (AI) has got a lot of attention, especially for the success of deep learning in addressing problems that were considered problematic before.
Topics of Interest
The proposed special session provides a forum for bringing together researchers from academia and industry to explore and present their findings in Artificial Intelligence, cybersecurity issues of IoT, and AmI. The participants are encouraged to discuss the theories, systems, technologies, and approaches for testing and validating them on challenging real-world, safety-critical applications. Thus, suggested topics include, but are not limited to, the following points:
Formal security and resilience analysis on AI.
- IoT security, trust, and trustworthy
- Secure and privacy-preserving IoT communications
- Cognitive models and bio-inspired AI.
- AI-assisted critical infrastructure security.
- Applied cryptography for IoT and AmI.
- Security and privacy of AmI.
- Applications of formal methods to IoT and AmI security.
- Blockchain for trustworthy AmI-based applications.
- IoT and embedded systems security.
- Cyber threat intelligence for IoT and AmI.
- Privacy-Preserving Machine Learning for IoT.
- Federated learning for IoT networks.
Paper Submission Deadline
- Deadline for Paper Submissions: July 9th, 2023
- Acceptance Notification: July 31st, 2023
- Deadline for Camera-Ready Paper Submissions: August 20th, 2023
- Deadline for Presentation Submissions: September 25th, 2023
Papers should be six (6) pages in length and follow the instruction provided for the main Conference. The conference allows up to two additional pages for a maximum length of eight (8) pages with payment of extra page charges once the paper has been accepted.
Please submit your paper for this Special Session using the link to eWorks:
If you have any questions, please contact Dr. Abdellah Chehri: email@example.com
Abdellah Chehri: Royal Military College of Canada
Dr. Abdellah Chehri (Senior Member IEEE) is an Associate Professor at the Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC), Kingston, Ontario. Dr. Chehri is a co-author of more than 200 peer-reviewed publications in established journals and conference proceedings sponsored by established publishers such as IEEE, ACM, Elsevier, and Springer. Dr. Chehri has served on roughly thirty conference and workshop program committees. In addition, he served as guest/associate editor for several well-reputed journals. Additionally, he is a Senior Member of IEEE, a member of the IEEE Communication Society, IEEE Vehicular Technology Society (VTS), and IEEE Photonics Society.
Gwanggil Jeon: Incheon National University
Dr. Gwanggil Jeon (Senior Member IEEE) received his B.S., M.S., and Ph.D. degrees from Hanyang University, Korea, in 2003, 2005, and 2008, respectively. From 2009 to 2011, he was a postdoctoral fellow at the University of Ottawa, Canada, and from 2011 to 2012, he was an assistant professor at Niigata University, Japan. He is a professor at Xidian University, China and Incheon National University, South Korea. His research interests fall under the umbrella of image processing, deep learning, artificial intelligence, smart grid, and Industry 4.0
Muhammad Zeeshan Shakir: University of the West of Scotland
Dr Muhammad Zeeshan Shakir (Senior Member, IEEE) is currently a professor of wireless communications with the University of the West of Scotland (UWS), U.K., and received over £3m research funding from Innovate U.K., ERASMUS, QNRF, and Scottish Government. With more than 15 years of research expertise in design and development of digital technologies, he has published more than 150 research articles and contributed to ten books. He is a fellow of the Higher Education Academy, U.K., and an Active Member of the IEEE Communications Society. He is also a Royal Society Member of the Edinburgh Young Academy of Scotland through a country-wide competitive selection process for building artificial intelligence capacity across Scotland. He is the Chair of the IEEE Communications Society Emerging Technologies Committee on Backhaul/Fronthaul and the Public Safety Technology Committee Informatics. He has been a frequent Keynote Speaker/a Tutorial Speaker with IEEE Flagship Conferences, such as IEEE Globecom and ICC, and international events.
Imran Ahmed: Anglia Ruskin University
Dr Imran Ahmed (Senior Member, IEEE) received a Ph.D. degree in Computer Science from the University of Southampton, Southampton, U.K, in 2014. Currently, he is working with Anglia Ruskin University, UK. His research interests mainly include artificial intelligence, deep learning, machine learning, data science, computer vision. He has attended several international conferences in these areas. He has published numerous articles in refereed journals and conference proceedings, including IEEE Access, IEEE Transactions on Industrial Informatics, IEEE Internet of Things Journal