Securing Smart Homes: Machine Learning Solutions for IoT Cyber Threats

Smart home technologies simplify light and temperature management, making life easier. These technologies provide smartphone-based home management. IoT improves smart home equipment efficiency and customization. IoT gadgets are linked, so hackers may access security cameras, personal data, and smart locks and thermostats, endangering people and property. A solid cybersecurity policy helps manage these security issues. Cyber threat detection early allows rapid reaction and mitigation, minimizing harm and preventing escalation. Active security breach detection reduces sensitivity and improves cybersecurity resilience by limiting consequences, forbidding illegal access, and protecting vital data. To increase smart home security, this initiative uses Aposemat IoT -23 dataset and machine learning methods. We tested XGBoost, Simple Neural Network, GaussianNB, and Logistic Regression for illegal access and data injection detection. XGBoost and Simple Neural Network show their abilities to safeguard smart home networks by identifying and preventing dangerous activity with over 99% accuracy. These results illustrate how efficiently machine learning algorithms cover IoT devices in smart homes, hence delivering a safer and more trustworthy environment for consumers.