TY  - EJOU
AU  - Saqaeeyan, Sasan 
AU  - Javadi, Hamid Haj Seyyed 
AU  - Amirkhani, Hossein 

TI  - A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2019
VL  - 121
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - Smart homes
KW  -  sensory data
KW  -  anomaly detection
KW  -  Bayesian networks
KW  -  ensemble method

DO  - 10.32604/cmes.2019.07848