Training and implementing a steady state log analyzer

A system, comprising:

• at least one processor;
• memory in electronic communication with the at least one processor;
• and
• instructions stored in the memory, the instructions being executable by the at least one processor to:
• identify a steady state log file for an application associated with normal operation of the application over a period of time;
• applying an encoding model to the steady state log file to generate a multi-dimensional representation of the steady state log file;
• generating an outlier detection model trained to determine a plurality of outlier scores for a plurality of lines of a given log file based on the multi-dimensional representation of the steady state log file being associated with normal operation of the application, wherein an outlier score indicates a predicted probability that a given line from the plurality of lines of the given log file is an outlier from normal operation of the application;
• and
• applying the outlier detection model to an input log file to generate a plurality of outputs indicating lines of the input log file that are predicted to be outliers from normal operation of the application.

A non-transitory computer readable medium storing instructions thereon that, when executed by at least one processor, causes a computing device to:

• identify a steady state log file for an application associated with normal operation of the application over a period of time;
• applying an encoding model to the steady state log file to generate a multi-dimensional representation of the steady state log file;
• generating an outlier detection model trained to determine a plurality of outlier scores for a plurality of lines of a given log file based on the multi-dimensional representation of the steady state log file being associated with normal operation of the application, wherein an outlier score indicates a predicted probability that a given line from the plurality of lines of the given log file is an outlier from normal operation of the application;
• and
• applying the outlier detection model to an input log file to generate a plurality of outputs indicating lines of the input log file that are predicted to be outliers from normal operation of the application.