Securing Internet Of Things-Assisted Consumer Electronics Using Blockchain With Deep Learning-Based User Authentication

Internet of Things (IoT)-assisted consumer electronics refer to common devices that are improved with IoT technology, allowing them to attach to the internet and convey with other devices. These smart devices contain smart home systems, smartphones, wearables, and appliances, which can be monitored remotely, gather, and share data, and deliver advanced functionalities like monitoring, automation, and real-time upgrades. Safety in IoT-assisted consumer electronics signifies a cutting-edge technique to improve device safety and user authentication. Iris recognition (IR) is a biometric authentication technique that employs the exclusive patterns of the iris (the colored part of the eye that surrounds the pupil) to recognize individuals. This method has gained high popularity owing to the uniqueness and stability of iris patterns in finance, healthcare, industries, complex systems, and government applications. With no dual irises being equal and small changes through an individual’s lifetime, IR is considered to be more trustworthy and less susceptible to exterior factors than other biometric detection models. Different classical machine learning (ML)-based IR techniques, the deep learning (DL) approach could not depend on feature engineering and claims outstanding performance. In this paper, we propose an enhanced IR using the Remora fractals optimization algorithm with deep learning (EIR-ROADL) technique for biometric authentication. The main intention of the EIR-ROADL model is to project a hyperparameter-tuned DL technique for automated and accurate IR. For securing consumer electronics, blockchain (BC) technology can be used. In the EIR-ROADL technique, the EIR-ROADL approach uses the Inception v3 method for the feature extraction procedures and its hyperparameter selection process takes place using ROA. For the detection and classification of iris images, the EIR-ROADL technique applies the variational autoencoder (VAE) model. The experimental assessment of the EIR-ROADL algorithm can be executed on benchmark iris datasets. The experimentation outcomes indicated better IR outcomes of the EIR-ROADL methodology with other current approaches and ensured better biometric authentication results.