Smart Home Secure Network using Decentralized Infrastructure and Machine Learning Techniques

Security vulnerabilities in telecommunications networks have become an important issue due to the significant growth of gadgets for smart homes. and its connectivity via the Internet of Things (IoT) This research supports the smart home network learning engine using secure communications based on the blockchain system and evaluation layers based on cloud systems for determine their priorities and classify them into three kinds of coefficients (T): Avoid T. Smart T and Mod T. The learning engine uses a neural network to train and classify these types and the blockchain helps layer make more effective decisions. Key contributions of this study include implementing a secured blockchain layers for user authenticating and creating a ledger for the communication networks. Besides, using of a cloud-depend datas assessment layers, the development of the SI-depend teaching algorithm, and the complementarity of a neural engine for accurate classification are highlighted. The suggested algorithm excels the sequentially deep learning machine (RTS-DELM), data fusion techs., and IoT AL. technologies in affording electronic information engineering and improvement strategy analysis. This is achieved by reducing computational complication, false authentication rates, and quality parameters, ensuring a safty and effective smart home communication network that improves human life style.