Detecting abnormal usages in intelligent systems, especially in smart home systems, is an important task. By exploring log data, useful information and patterns can be discovered which may help users/organizations to better understand the usage of their appliances and to distinguish unnecessary usages as well as abnormal problems which can cause waste, damages, or even fire.

This work proposes several methods which can be used to detect the abnormal usages of appliances in smart home system and proposes a parameter tuning strategy to optimize the model’s accuracy. The proposed methods are validated by using a real dataset. Experimental results show that these approaches perform nicely and could be applied for practice.