Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network
Tyler Cultice, Dan M. Ionel, Himanshu Thapliyal
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.
Topics & Concepts
AutoencoderAnomaly detectionComputer scienceSmart gridHome automationWireless sensor networkConvolutional neural networkDeep learningData modelingAnomaly (physics)Data collectionReal-time computingComputer securityArtificial intelligenceData miningEmbedded systemComputer networkEngineeringDatabaseTelecommunicationsMathematicsElectrical engineeringCondensed matter physicsStatisticsPhysicsAnomaly Detection Techniques and ApplicationsNetwork Security and Intrusion DetectionSmart Grid Security and Resilience