Online Fusion of Incremental Learning for Wireless Sensor Networks

Ever-more ubiquitous embedded systems provide us with large amounts of data. Performing analysis close to the data source allows for data reduction while giving information when unexpected behavior (i.e. Anomalies in the system under observation) occurs. This work presents a novel approach to online anomaly detection, based on an ensemble of classifiers that can be executed on distributed embedded systems. We consider both single and multi-dimensional input classifiers that are based on prediction errors. Predictions of single-dimensional time series input come from either a linear function model or general statistics over a data window. Multi-dimensional input stems from current and historical sensor values as well as predictions. We combine the classifier outputs in the ensemble using a heuristic method and Fisher's combined probability test. The proposed framework is tested thoroughly using synthetic and real-world data. The results are compared to known methods for anomaly detection on limited-resource systems. While individual classifiers perform comparably to known methods, our results show that using an ensemble of classifiers increases the overall detection of anomalies considerably.