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A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

Sasan Saqaeeyan1, Hamid Haj Seyyed Javadi1,2,*, Hossein Amirkhani1,3
1 Department of Computer Engineering, Borujerd Branch, Islamic Azad University, Borujerd, Iran.
2 Department of Mathematics and Computer Science, Shahed University, Tehran, Iran.
3 Computer Engineering and Information Technology Department, University of Qom, Qom, Iran.
* Corresponding Author: Hamid Haj Seyyed Javadi. Email: .

Computer Modeling in Engineering & Sciences 2019, 121(3), 815-834. https://doi.org/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 the effectiveness of the proposed method by reducing false positives and increasing true positives.

Keywords

Smart homes, sensory data, anomaly detection, Bayesian networks, ensemble method.

Cite This Article

Saqaeeyan, S., Haj, H., Amirkhani, H. (2019). A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes. CMES-Computer Modeling in Engineering & Sciences, 121(3), 815–834.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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