ITAD
Youjin Shin, Sang Yup Lee, Shahroz Tariq, Myeong Shin Lee, Okchul Jung, Daewon Chung, Simon S. Woo
Abstract
Reducing false positives while detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems. Undesired false positives can be costly for such systems, bringing the operation to a halt for human experts to determine if the anomalies are true anomalies that need to be mitigated. Although rule-based or machine learning-based anomaly detection approaches have been studied, a tensor-based decomposition method has not been extensively explored. In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system with the goal of minimizing false positives. We construct 3rd-order tensors with telemetry data collected from the Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and calculate the anomaly score using one of the component matrices obtained by applying CANDECOMP/PARAFAC decomposition to detect anomalies. Our result shows that our tensor-based approach outperforms existing methods, achieving higher accuracy and lower false positive rates. And we successfully deployed our anomaly detection system in real KOMPSAT-2 mission operation.