Decentralized IDS for IoT Using Blockchain- Backed Federated Learning

As the number of IoT devices continues to surge, ensuring the security of these devices and networks becomes increasingly difficult. Traditional centralized Intrusion Detection Systems (IDS) are not well-suited for the distributed and heterogeneous nature of IoT networks. This paper proposes a Decentralized IDS for IoT using Blockchain-Backed Federated Learning (FL) to provide a scalable, secure, and privacy-preserving solution for intrusion detection in IoT networks. The proposed system uses blockchain to verify and record model updates in a trustless manner, while federated learning allows for distributed training without sharing raw data. This combination ensures privacy and security while maintaining the efficiency and accuracy of intrusion detection. We discuss the architecture, implementation, and evaluation of the proposed system and demonstrate its effectiveness in real-world IoT environments.