The Challenge of Adversarial Machine Learning in the Context of Smart Homes

Smart homes are more safe, convenient, and automated than ever, changing how we interact with them. As these systems employ machine learning increasingly, aggressive machine learning is a concern. This article describes three ways to safeguard smart homes from dangerous individuals. Robust Net Guard, Secure Sense, Ethical Guardian. Robust Net Guard begins these approaches. It strengthens smart home machine learning models by adding safety layers to deep neural networks. This improves model accuracy and resistance to unwanted modifications. The second smart home security method is Secure Sense. Finding suspicious activity and real-time monitoring let it identify and stop security threats rapidly. The final method is Ethical Guardian. It prioritizes ethics and ensures fairness, transparency, and accountability in smart home machine learning models. It addresses prejudice, interpretability, and morality to moralize smart homes. This research compares these three approaches in accuracy, precision, memory, F1 Score, and computer work. Results are shown using line charts and box graphs. These evaluations help us understand each plan's merits and downsides and prepare for future smart home system assaults. As smart home technology evolves, robust, moral machine learning models are needed. This paper addresses hostile machine learning challenges in smart home technology, contributing to the safety, privacy, and moral debate.