Robust Anomaly Detection for Large-Scale Sensor Data

Large scale sensor networks are ubiquitous nowadays. An important objective of deploying sensors is to detect anomalies in the monitored system or infrastructure, which allows remedial measures to be taken to prevent failures, inefficiencies, and security breaches. Most existing sensor anomaly detection methods are local, i.e., they do not capture the global dependency structure of the sensors, nor do they perform well in the presence of missing or erroneous data. In this paper, we propose an anomaly detection technique for large scale sensor data that leverages relationships between sensors to improve robustness even when data is missing or erroneous. We develop a probabilistic graphical model-based global outlier detection technique that represents a sensor network as a pairwise Markov Random Field and uses graphical model inference to detect anomalies. We show our model is more robust than local models, and detects anomalies with 90% accuracy even when 50% of sensors are erroneous. We also build a synthetic graphical model generator that preserves statistical properties of a real data set to test our outlier detection technique at scale.