| Title | Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Cultice, Tyler, Ionel, Dan, Thapliyal, Himanshu |
| Conference Name | 2020 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) |
| Date Published | dec |
| Keywords | anomaly detection, cybersecurity, Data collection, machine learning, Metrics, Neural networks, pubcrawl, resilience, Resiliency, Scalability, security, Sensor systems, Sensors, smart grid security, Smart grids, smart home, Smart homes |
| Abstract | We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home. |
| DOI | 10.1109/iSES50453.2020.00026 |
| Citation Key | cultice_smart_2020 |
Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network