Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering

Symbolic dynamic analysis of complex systems for anomaly detection

This paper presents a novel concept of anomaly detection in complex dynamical systems using tools of Symbolic Dynamics, Finite State Automata, and Pattern Recognition, where time-series data of the observed variables on the fast time-scale are analyzed at slow time-scale epochs for early detection of (possible) anomalies. The concept of anomaly detection in dynamical systems is elucidated based...

Anomaly Detection via Optimal Symbolic Observation of Physical Processes∗

The paper introduces a symbolic, discrete-event approach for online anomaly detection. The approach uses automata representations of the underlying physical process to make anomaly occurrence determination. Automata may represent a discrete-event formulation of the operation of the monitored system during both normal and abnormal conditions. Automata may also be constructed from generated symbo...

Statistical estimation of multiple parameters via symbolic dynamic filtering

This paper addresses statistical estimation of multiple parameters that may vary simultaneously but slowly relative to the process response in nonlinear dynamical systems. The estimation algorithm is sensor-data-driven and is built upon the concept of symbolic dynamic filtering for real-time execution on limited-memory platforms, such as local nodes in a sensor network. In this approach, the be...

Understanding Anomaly Detection Techniques for Symbolic Sequences

Adaptive Anomaly Detection via Self-calibration and Dynamic Updating

The deployment and use of Anomaly Detection (AD) sensors often requires the intervention of a human expert to manually calibrate and optimize their performance. Depending on the site and the type of traffic it receives, the operators might have to provide recent and sanitized training data sets, the characteristics of expected traffic (i.e. outlier ratio), and exceptions or even expected future...