Detecting Anomalies within Smart Buildings using Do-It-Yourself Internet of Things
This work addresses anomaly detection for smart building security, but it appears incremental as it focuses on practical considerations and data handling without introducing major new methods.
The paper tackled anomaly detection in smart buildings using DIY IoT devices, achieving effective identification of point, contextual, and combined anomalies through data gathered from multiple sensors.
Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.