Meta Monitoring System (MMS)

Meta Monitoring System (MMS) was developed as an add-on to NASA Ames patented Inductive Monitoring System (IMS), which estimates deviation from normal system operations. MMS helps to interpret deviation scores and determine whether anomalous behavior is transient or systemic. MMS has two phases: a model-building training phase, and a monitoring phase. MMS not only uses deviation scores from nominal data for training but can also make limited use of results from anomalous data. The invention builds two models: one of nominal deviation scores and one of anomalous deviation scores, each consisting of a probability distribution of deviation scores. After the models are built, incoming deviation scores from IMS (or a different monitoring system that produces deviation scores) are passed to the learned model, and probabilities of producing the observed deviation scores are calculated for both models. In this fashion, users of MMS can interpret deviation scores from the monitoring system more effectively, reducing false positives and negatives in anomaly detection. Note: Patent license only; no developed software available for licensing Various software approaches exist that determine whether a given instrumented system is experiencing anomalous behavior. Many anomaly detection systems simply generate some type of deviation score, then rely on a subject matter expert to evaluate the data and recommend a course of action. These evaluations are often nontrivial and can lead to false alarms. NASAs Meta Monitoring System (MMS) is an add-on to these types of anomaly detection systems that helps one to better interpret such deviation scores and determine whether detected anomalous behavior is transient or systemic. While MMS was developed as an add-on to NASAs patented Inductive Monitoring System (IMS) anomaly detection software, which could be used alongside this invention to provide a complete anomaly detection solution, it can integrate with any anomaly detection system that generates deviation scores.