A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior

TLDR

Smart homes, which automate lighting, temperature, security cameras, and appliances, are increasingly popular but become vulnerable targets as their internet‑connected devices and sensors are easily attacked. The study aims to reduce smart‑home security risks by developing adaptive systems that analyze user behavior and environmental context to predict and prevent future attacks. To achieve this, the authors propose a Hybrid Intrusion Detection system that combines multiple machine‑learning classifiers—random forest, XGBoost, decision tree, K‑nearest neighbors—with misuse detection techniques.

Abstract

With technology constantly becoming present in people's lives, smart homes are increasing in popularity. A smart home system controls lighting, temperature, security camera systems, and appliances. These devices and sensors are connected to the internet, and these devices can easily become the target of attacks. To mitigate the risk of using smart home devices, the security and privacy thereof must be artificially smart so they can adapt based on user behavior and environments. The security and privacy systems must accurately analyze all actions and predict future actions to protect the smart home system. We propose a Hybrid Intrusion Detection (HID) system using machine learning algorithms, including random forest, X gboost, decision tree, K -nearest neighbors, and misuse detection technique.