Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG...

Area of Science:

• Computer Science
• Artificial Intelligence
• Cybersecurity

Background:

• The Industrial Internet of Things (IIoT) presents significant opportunities but faces data security and privacy challenges.
• Current authentication mechanisms in IIoT struggle with scalability and adaptability to evolving user demands.
• Real-time data collection in industrial applications necessitates robust security and privacy solutions.

Purpose of the Study:

• To propose a novel Federated Transfer Learning (FTL) framework for authentication and privacy preservation in IIoT.
• To address the limitations of existing authentication methods in dynamic industrial environments.
• To enhance the security and privacy standards for industrial applications through advanced algorithms.

Main Methods:

• Implementation of Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) algorithm.
• Utilization of FT-Block (Federated Transfer Learning Blockchain) integrating multiple blockchains for security.
• Development of an authentication mechanism based on transfer learning for regional user authentication.

Main Results:

• The proposed S-TD3 algorithm effectively trains user models for region-specific authentication.
• FT-Block enhances privacy and security across diverse industrial applications.
• The framework facilitates secure and scalable data transfer between devices in industrial operations.

Conclusions:

• The developed FT-Block framework with the S-TD3 algorithm offers a viable solution for IIoT security and privacy.
• This approach improves authentication robustness and adaptability in industrial settings.
• The study demonstrates the effectiveness of federated learning and blockchain in securing IIoT ecosystems.