Blockchain-Assisted Secure Smart Home Network Using Gradient-Based Optimizer with Hybrid Deep Learning Model

Abstract

The Internet of Things (IoT) refers to a technology enabler to enhance the urban physical architecture and render public services. But, public access to accumulated heterogeneous IoT urban information is prone to hackers attacking connected devices to the internet intellectual property as well. IoT security serves a dynamic part in the smart city. Some IoT devices are connected in smart homes, and these connections were centred on gateways. In smart homes, the gateways gain a lot of significance; but their centralized structure causes many security vulnerabilities like availability, integrity, and certification. Unified 'cloud-like' computing networks and Blockchain (BC) type systems should be used to sort out these problems. Therefore, this article develops a Blockchain-Assisted Secure Smart Home Network using Gradient Based Optimizer with Hybrid Deep Learning (BSSHN-GBOHDL) model. The presented BSSHN-GBOHDL technique employs BC technology to improve the confidentiality of the data in the smart home environment. In addition, the BSSHN-GBOHDL technique identifies malicious activities in the smart home environment via three sub-processes namely data preprocessing, hybrid deep learning (HDL)-based malicious activity classification, and GBO-based hyperparameter tuning. The GBO algorithm assists in the proficient hyperparameter selection of the HDL model, which aids in accomplishing increased detection efficiency. The experimental validation of the BSSHN-GBOHDL approach is tested on a benchmark NSL-KDD dataset with 65495 normal and 60743 attack samples. The results highlight the betterment of the BSSHN-GBOHDL approach over other recent methods with maximum accuracy of 98.29%.