US 2026/0189593 A1Published application

Anomaly Detection In Copper Networks

AssigneeFrontier Communications Holdings, LLC

InventorVictor C. Le

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Abstract

Techniques for detecting one or more anomalies in a copper network include extracting a plurality of features from historical data of a copper network, transforming the historical data to generate additional features, and training a machine learning model on the historical data, the extracted features, and the additional features to discover prevailing, normal, or typical historical behaviors or features of the copper network and/or of its components while operating in a target or desired operating range. An anomaly detector of the copper network may utilize the trained ML model on current copper network data to detect one or more anomalies within the current copper network, and initiate respective mitigating actions. The ML model may be optimized by validating the trained ML model and tuning hyperparameters to optimize performance. In an example, optimizing the trained ML model may be based on excess mass curves.

Claims29

Dependent claims

A computer-implemented method for detecting anomalies occurring in a copper network, the method comprising:

obtaining, by one or more processors, historical data associated with a copper network, the historical data including data indicative of a multiplicity of historical copper network equipments, a multiplicity of historical operating behaviors of the copper network which have occurred, and a multiplicity of historical events corresponding to the copper network which have occurred;
extracting, by the one or more processors and from the historical data, a plurality of copper network equipment features, each copper network equipment feature indicative of a respective behavior of a respective copper network equipment, and at least one copper network equipment feature including at least one lagged feature, the at least one lagged feature including at least one feature that occurs over time;
transforming, by the one or more processors, at least one extracted copper network equipment feature, thereby generating at least one additional copper network equipment feature;
training, by the one or more processors, a machine learning (ML) model on (i) the extracted plurality of copper network equipment features including the at least one lagged feature, and (ii) the at least one additional copper network equipment feature to discover one or more historical behaviors of the copper network, the one or more historical behaviors being indicative of the copper network operating within a target operating range;
detecting, by the one or more processors based on current data of the copper network and by utilizing the trained ML model, one or more anomalies occurring in the copper network;
and
initiating, by the one or more processors and responsive to the detecting, a mitigating action for the detected one or more anomalies occurring in the copper network.

4 more elements

A system for detecting anomalies in a copper network, the system comprising:

one or more processors;
and
one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to:
obtain historical data associated with a copper network, the historical data including data indicative of a multiplicity of historical copper network equipments, a multiplicity of historical operating behaviors of the copper network which have occurred, and a multiplicity of historical events corresponding to the copper network which have occurred;
extract, from the historical data, a plurality of copper network equipment features, each copper network equipment feature indicative of a respective behavior of a respective copper network equipment, and at least one copper network equipment feature including at least one lagged feature, the at least one lagged feature including at least one feature that occurs over time;
transform at least one extracted copper network equipment feature to generate at least one additional copper network equipment feature;
train a machine learning (ML) model on (i) the extracted plurality of copper network equipment features including the at least one lagged feature, and (ii) the at least one additional copper network equipment feature to discover one or more historical features of the copper network that are indicative of the copper network operating within a target operating range;
detect, based on current data of the copper network and by using the trained ML model, one or more anomalies occurring in the copper network;
and
initiate a mitigating action for the detected one or more anomalies occurring in the copper network.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/324,339Filed 10 Sep 2025
Priority: 30 Dec 2024
Published: 2 Jul 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification5 codes

H04L 63/1433
For detecting or protecting against malicious traffic: vulnerability analysis
H04L 12/2878
Operational details of access network equipments: access multiplexer, e.g. DSLAM
H04L 41/142
Network analysis or design: using statistical or mathematical methods
H04L 41/16
Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks: using machine learning…