Sasan Saqaeeyan1, Hamid Haj Seyyed Javadi1,2,*, Hossein Amirkhani1,3
CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 815-834, 2019, DOI:10.32604/cmes.2019.07848
Abstract Anomaly detection in smart homes provides support to enhance the health and
safety of people who live alone. Compared to the previous studies done on this topic, less
attention has been given to hybrid methods. This paper presents a two-steps hybrid
probabilistic anomaly detection model in the smart home. First, it employs various algorithms
with different characteristics to detect anomalies from sensory data. Then, it aggregates their
results using a Bayesian network. In this Bayesian network, abnormal events are detected
through calculating the probability of abnormality given anomaly detection results of base
methods. Experimental evaluation of a real dataset indicates… More >
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