System and method for detecting platform anomalies through neural networks
AssigneeApcera, Inc.
InventorsYouzhi Zou, Derek Collison
Life of this patent
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Abstract
A system and method for detecting behavior of a computing platform that includes obtaining platform data; for each data motif identifiers in a set data motif identifiers, performing data motif detection on data in an associated timescale, wherein a first data motif identifier operates on data in a first timescale, wherein a second data motif identifier operates on data in a second timescale, wherein the first timescale and second timescale are different; in a neural network model, synthesizing platform data anomaly detection with at least a set of features inputs from data motif detection of the set of motif identifiers; and signaling if a platform data anomaly is detected through the neural network model.
Claims19
A system for monitoring usage of a computing platform comprising:
- data ingester that collects time-based data of a computing platform, wherein the time-based data includes data of a plurality of data types;
- a set of motif identifier modules, wherein each motif identifier is configured to apply a machine learning process within one timescale of the data and output a motif signal, the set of motif identifier modules comprising a raw motif identifier module that operates on data in a base timescale, at least one motif identifier module that operates on data from a substantially weekly timescale, at least one motif identifier that operates on data from a substantially yearly timescale,
- a set of data samplers, wherein a data sampler of a one timescale couples a sampled data output to at least one motif identifier of the same the one timescale, and
- a neural network model that includes feature inputs of at least one layer coupled to the motif signal outputs, a combined motif layer, and including at least one output signal of the operational status of the computing platform.
A method for detecting behavior of a computing platform comprising:
- obtaining platform data;
- for each data motif identifiers in a set data motif identifiers, performing data motif detection on data in an associated timescale, wherein a first data motif identifier operates on data in a first timescale, wherein a second data motif identifier operates on data in a second timescale, wherein the first timescale and second timescale are different;
- in a neural network model, synthesizing platform data anomaly detection with at least a set of features inputs from data motif detection of the set of motif identifiers;
- and
- signaling if a platform data anomaly is detected through the neural network model.
The method for predicting data comprising:
- obtaining platform data;
- for each data motif identifiers in a set data motif identifiers, performing data motif detection on data in an associated time scale, wherein a first data motif identifier operates on data in a first timescale, wherein a second data motif identifier operates on data in a second timescale, wherein the first timescale and second timescale are different;
- in a neural network model, synthesizing platform data motif prediction with at least a set of features inputs from data motif detection of the set of motif identifiers;
- and
- signaling a platform data prediction generated through the neural network model.
Dependencies are inferred from the claim text.