Autonomous Anomaly Detection via Physics-Regularized Machine Learning
Abstract
Manual inspection of telemetry data in the search for anomalies is a time-consuming threat detection technique. Most multi-signal systems send backextensive data that a single person cannot easily monitor in real time. Machine learning techniques that autonomously scan data and flag anomalies are attractive alternatives. The autonomous anomaly detection problem can be divided into two sub-problems: regression analysis and a classification process. In the regression analysis, a machine learning model is trained to reconstruct a given signal, and the classification process categorizes the reconstruction error as anomalous or nominal. This report examines the autonomous anomaly detection problem and proposes improvements to both the regression and classification sub-problems. With regard to the regression analysis, it was found that including the physics of the target signal in the machine learning model yielded a lower reconstruction error compared to a purely data-driven model. The classification approaches studied showed that cluster-based thresholding techniques accompanied by a pruning procedure can outperform non-parametric dynamic thresholds.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 22, 2023
- Accession Number
- AD1206049
Entities
People
- Felipe Giraldo-grueso
- Renato Zanetti
Organizations
- University of Texas at Austin