# Blockchain and Machine Learning Inspired Secure Smart Home Communication Network
###### Abstract  
With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings. blockchain; consensus protocol; dragonfly algorithm; Levenberg model; smart contract 20232361323/13/10  
allowing access to the authorized user. The fourth is \"Integration\", which helps minimize data losses and maintains the data in an accurate manner. The fifth is \"Availability\", which provides the available service access to authorized users, who are protected from threats. Thus, a smart home network can be sensitive to security threats due to the large number of connected devices [6]. In such cases, a supervised approach to data analysis generated by the IoT network could be quite useful. Swarm intelligence is adequate for solving NP-hard problems. These are mainly used for feature extraction and dimension reduction. Metaheuristic techniques have the potential to address multi-object problems. The population-based metaheuristic algorithm is one of the most effective optimization algorithm architectures. Different types of behaviors are observed in nature, and hence, different algorithms are studied, and the dragonfly algorithm is a powerful data selection tool that can be used to identify patterns and trends in data sets [7]. It is particularly useful for finding outliers and unusual data points. Additionally, some decentralized and deep learning frameworks have also been presented by the research community [8; 9; 10]. The proposed model represents a significant research contribution to the field of information engineering, as it outperforms existing methods, such as the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, data fusion techniques, and artificial intelligence Internet of Things technology, in terms of the computation complexity, false authentication rate, and qualitative parameters. With the evolution of blockchain-based communication, the communication data can be kept in a ledger-based record sheet, which is popularly known as a hyperledger in terms of Blockchain [11; 12]. This paper focuses on the establishment of a learning engine that is associated with a smart home communication network. To achieve this, a neural-based propagation engine was used for the decision-making of smart transactions, moderate transactions, and to-be-blocked transactions, as shown in Figure 1.  
Figure 1: The smart home application architecture. As shown in Figure 1, there are four layers in the application architecture.