TITLE:
A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior
AUTHORS:
Faisal Alghayadh, Debatosh Debnath
KEYWORDS:
Anomaly Detection, Smart Home Systems, Behavioral Patterns, Security, Threats
JOURNAL NAME:
Advances in Internet of Things,
Vol.11 No.1,
January
28,
2021
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.