US 12,067,489 B2
Distributed learning anomaly detector
Shankar Ananthanarayanan, Ashburn, VA (US); Nicole Eickhoff, Woodbridge, VA (US); Tim Herrmann, Bon Air, VA (US); Matthew Luebke, Front Royal, VA (US); and Mathew Maloney, Austin, TX (US)
Assigned to ScienceLogic, Inc., Reston, VA (US)
Filed by ScienceLogic, Inc., Reston, VA (US)
Filed on Dec. 2, 2021, as Appl. No. 17/540,421.
Application 17/540,421 is a continuation of application No. 16/856,905, filed on Apr. 23, 2020, granted, now 11,210,587.
Claims priority of provisional application 63/014,082, filed on Apr. 22, 2020.
Claims priority of provisional application 62/837,611, filed on Apr. 23, 2019.
Claims priority of provisional application 62/837,593, filed on Apr. 23, 2019.
Prior Publication US 2022/0092421 A1, Mar. 24, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01); H04L 9/40 (2022.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01); H04L 63/1425 (2013.01)] 26 Claims
OG exemplary drawing
 
1. A network comprising:
a non-transitory storage device that stores a portable encoding of an initial machine learning-trained hyperparameter data set parameterizing operating metrics and information characterizing operation of executing software components of at least one proto-typical network device as a Distributed Learning Anomaly Detector (DLAD) dynamic application configured to use the initial machine learning-trained hyperparameter data set parameterizing the operating metrics and information comprising at least one collected data set from at least one data source specified by the DLAD dynamic application; and
a machine-learning environment configured to incorporate information from the DLAD dynamic application including data from the at least one data source specified by the DLAD dynamic application,
the machine-learning environment configured to use, as initial parameters, the initial machine learning-trained hyperparameter data set for local machine learning using local data and a non-locally initialized model configured to model executing software component characteristics of devices, the initial machine learning-trained hyperparameter data set comprising a configuration for the non-locally initialized model;
the machine-learning environment being further configured to use (a) the initial machine learning-trained hyperparameter data set or a hyperparameter data set derived, at least in part, from the machine learning-trained hyperparameter data set and (b) the at least one collected data set from the at least one data source, to discover operational condition events,
wherein the DLAD dynamic application selectively instantiates at least one additional dynamic application based at least on part on the discovered operational condition events.