Enhancing Consumer Electronics Security in Smart Homes with t-SNE Feature Selection and Seagull Optimized Deep Learning Model

This work presents a t-SNE-based feature selection and Seagull Optimized Deep Learning Model to improve consumer electronics security in smart homes. Optimized using the Seagull Optimization Algorithm (SOA), the suggested Convolutional Neural Network (CNN) model showed exceptional performance with an accuracy of 92% in identifying cyber risks. The model routinely exceeded in both accuracy and loss measures when tested against conventional machine learning and deep learning models. T-SNE helped to choose important criteria that provide strong identification of harmful activity. Our method presents a major development in smart home security and a dependable instrument for reducing cybersecurity threats in a linked world.