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Perlich, C. (2010) Learning Curves in Machine Learning. In: Sammut, C. and Webb, G.I., Eds., Encyclopedia of Machine Learning, Springer, Boston, 10-50.
https://doi.org/10.1007/978-0-387-30164-8

has been cited by the following article:

• 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.