文章基本信息

标题：A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior
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作者：Faisal Alghayadh ; Debatosh Debnath
期刊名称：Advances in Internet of Things
印刷版ISSN：2161-6817
电子版ISSN：2161-6825
出版年度：2021
卷号：11
期号：1
页码：10-25
DOI：10.4236/ait.2021.111002
语种：English
出版社：Scientific Research Publishing
摘要：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.
关键词：Anomaly Detection;Smart Home Systems;Behavioral Patterns;Security;Threats