This is the source code that accompanies Iterpretable, Multidimensional, Multimodal Anomaly Detection for Detecting Device Failure (Sipple, 2020)
In this paper we propose a scalable, unsupervised approach for detecting anomalies in the Internet of Things (IoT). Complex devices are connected daily and eagerly generate vast streams of multidi- mensional telemetry. These devices often operate in distinct modes based on external conditions (day/night, occupied/vacant, etc.), and to prevent complete or partial system outage, we would like to recognize as early as possible when these de- vices begin to operate outside the normal modes.
We propose an unsupervised anomaly detection method that creates a negative sample from the positive, observed sample, and trains a classifier to distinguish between positive and negative sam- ples. Using the Concentration Phenomenon, we explain why such a classifier ought to establish suitable decision boundaries between normal and anomalous regions, and show how Integrated Gra- dients can attribute the anomaly to specific dimen- sions within the anomalous state vector. We have demonstrated that negative sampling with random forest or neural network classifiers yield signifi- cantly higher AUC scores compared to state-of- the-art approaches against benchmark anomaly detection datasets, and a multidimensional, multi- modal dataset from real climate control devices.
Finally, we describe how negative sampling with neural network classifiers have been successfully deployed at large scale to predict failures in real time in over 15,000 climate-control and power meter devices in 145 office buildings within the California Bay Area.