Blockchain-Empowered Security in Smart Home Networks: A Fusion of Machine Learning

The main difficulty with smart home technology utilisation is that it frequently results in a number of security problems. The anticipated widespread development and adoption of the IoT technology poses obstacles for private and secure communications. Blockchain-based solutions make decentralised security and privacy possible. The suggested private blockchain-based smart home network architecture in this study uses the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model to decide intrusion detection. In order to search for any dangerous actions, this research investigates the RTS-DELM technique utilised in blockchain-based smart homes. This proposed approach takes connection, security, and authenticity into consideration while carefully evaluating the Smart Home Architecture based on Blockchain. Furthermore, we show through simulated results that our technique’s overheads are negligible considering the privacy and security it offers.