Patent ID: 11829229

Abstract:
A computing method and device for detecting an anomaly event in a hardware- based machine learning anomaly event detector. Data is received from a plurality of input/output interfaces (I/O) or internal functions, wherein the received data of each I/O is associated with a certain sensor or effector. The received data from the plurality of I/O's is analyzed to determine the occurrence of an anomaly event for data from one of the plurality of I/O's. This analysis includes using machine learning techniques dynamically programmed to detect an anomaly event for the I/O data being analyzed by using predetermined parameter values. The predetermined parameter values are retrieved from memory, and are associated with the I/O data being analyzed. Stored in memory for subsequent analysis is at least a portion of the received data from the I/O data being analyzed accompanied with associated neural network output when it is determined an anomaly event occurred for the I/O data being analyzed using the machine learning techniques.