Naïve Bayesian Approach to Intruder Detection in Smart Homes Using Sensor Networks

We used camera, light, door, and window sensors, and day of the week, duration, and activation time parameters for a Naïve Bayesian approach to intruder detection in a smart home. A nine-week normal and a two-week intruder behavior data were extracted using sensor devices. SmartThings SmartApp was used to gather the sensor data which were published to Google Sheets, then exported to Excel and Python for pre-processing, providing a dataset size of 509, consisting of 284 normal and 225 intruder data. The data had an 80/20 train-test split and was fitted to the Naïve Bayes model. By identifying behavioral patterns and increasing parameters from the state-of-the-art, the model’s accuracy improved to 94.12% and achieved an F2-score of 93.18%. This study has shown that increasing sensor size could significantly improve intruder detection. Future work could include the analysis of the contribution of each sensor to provide informed decision to system designers.