Authors: Mohamed Amine Ferrag, Othmane Friha, Burak Kantarci, Norbert Tihanyi, Lucas Cordeiro, Merouane Debbah, Djallel Hamouda, Muna Al-Hawawreh, Kim-Kwang Raymond Choo

Published on: June 17, 2023

Impact Score: 8.38

Arxiv code: Arxiv:2306.10309

Summary

• What is new: This paper presents a comprehensive review of the latest research on edge learning vulnerabilities and defenses, specifically geared towards the 6G-enabled IoT, which has not been extensively covered in previous surveys.
• Why this is important: The limitations of 5G in supporting IoT applications and the emerging challenges in securing next-generation networks like 6G from various attacks.
• What the research proposes: A detailed survey of attacks on machine learning models in the context of 6G IoT and a taxonomy of state-of-the-art defense mechanisms to protect against these vulnerabilities.
• Results: A holistic overview and comparison of existing research in the area of 6G IoT security, focusing on edge learning vulnerabilities and defenses.

Technical Details

Technological frameworks used: Edge learning for 6G-enabled IoT

Models used: Centralized, federated, and distributed learning modes

Data used: Not specified

Potential Impact

Telecommunications, IoT service providers, cybersecurity firms, and companies involved in developing autonomous systems and the Metaverse.

Want to implement this idea in a business?

We have generated a startup concept here: EdgeGuard AI.