Ensuring Privacy and Security of IoT Networks Utilizing Blockchain and Federated Learning

The Internet of Things (IoT) has become a game-changing technology, bridging the gap between the real and virtual worlds and allowing for smooth data transfer and communication between linked objects. The other two potential technologies are blockchain (BC) and artificial intelligence (AI), whose application areas are incredibly diverse and which may perform best when combined. Since some traditional machine learning (ML) techniques have limitations, this article proposed using distributed machine learning techniques such as federated learning and blockchain to build a more reliable and secure IoT network that will be better protected and less susceptible to outside intrusions. As an alternative to centralized cloud storage, we also recommended using decentralized data storage techniques like the InterPlanetary File System (IPFS) and Hyperledger Fabric (HLF). Additionally, as a proof of concept, we deployed our model using Ethereum Smart Contracts (SC). Using the well-known cybersecurity dataset known as Edge-IIoTset, we utilized both centralized and federated machine learning models to evaluate the efficiency of the suggested approach. The experimental results and successful deployment of Smart Contracts demonstrate that employing Blockchain and distributed storage systems is preferable for safeguarding IoT networks.