Ingress detection and localization

1 . A method for ingress detection and localization within a customer premises equipment network, comprising:

collecting data associated with customer premises equipment activities within a customer premises equipment network;

detecting, with a machine learning engine, customer premises equipment nodes within the collected data that are generating ingress data above a first predetermined level;

identifying locations of ingress data generation within the detected customer premises equipment nodes using a smart amplifier radio frequency tray, wherein the smart amplifier radio frequency tray is connected between the customer premises equipment nodes and a network node that connects the customer premises equipment nodes to a cable modem termination system;

detecting, by the machine learning engine, identified ingress data generation locations generating ingress data exceeding a second predetermined level; and

reducing ingress data generation from the detected identified locations.

2 . The method of claim 1 , wherein the machine learning engine detects customer premises equipment nodes generating ingress data above a first predetermined level and detects identified ingress generation locations generating ingress exceed a second predetermined level based on data clustering and data layering.

3 . The method of claim 1 , wherein the detection of locations that are generating excessive ingress is based on subgroup discovery and analytics (SCAN).

4 . The method of claim 3 , wherein the detection of locations that are generating excessive ingress is based on applying support vector machine (SVM) analysis on the SCAN results.

5 . The method of claim 1 , wherein the machine learning engine is trained by rules input to the machine learning engine and by capturing and analyzing data received from the customer premises equipment network.

6 . The method of claim 5 , wherein the machine learning engine is further trained by cable modem termination system data and by cable modem data.

7 . The method of claim 1 , wherein reducing ingress data generation comprises one or more of:

reducing a data speed of one or more of the detected identified ingress data generation locations;

transmitting self-repair signals to at least one of the detected identified ingress data generation locations; or

transmitting detected identified ingress data generation location data to a service technician.

8 . The method of claim 1 , wherein the locations of the generation of ingress is determined based on the data collected from the smart amplifier radio frequency tray.

9 . A system for ingress detection and localization within a customer premises equipment network, comprising:

a processing device;

a machine learning engine; and

non-transitory computer readable data storage media storing instructions that, when executed by the processing device, cause the processing device to detect and localize ingress noise within a customer premises equipment network, the ingress detection and localization system operative to:

collect data associated with customer premises equipment activities within a customer premises network;

detect, with a machine learning engine, customer premises equipment nodes within the collected data that are generating ingress data above a first predetermined level;

identify locations of ingress data generation within the detected customer premises equipment nodes using a smart amplifier radio frequency tray, wherein the smart amplifier radio frequency tray is connected between the customer premises equipment nodes and a network node that connects the customer premises equipment nodes to a cable modem termination system;

detect, by the machine learning engine, identified ingress generation locations generating ingress data exceeding a second predetermined level; and

reduce ingress data generation from the detected identified locations.

10 . The system of claim 9 , wherein the machine learning engine detects customer premises equipment nodes generating ingress data above a first predetermined level and detects identified ingress generation locations generating ingress exceed a second predetermined level based on data clustering and data layering.

11 . The system of claim 9 , wherein the detection of locations that are generating excessive ingress is based on subgroup discovery and analytics (SCAN).

12 . The system of claim 11 , wherein the detection of locations that are generating excessive ingress is based on applying support vector machine (SVM) analysis on the SCAN results.

13 . The system of claim 9 , wherein the machine learning engine is trained by rules input to the machine learning engine and by capturing and analyzing data received from the customer premises equipment network.

14 . The system of claim 13 , wherein the machine learning engine is further trained by cable modem termination system data and by cable modem data.

15 . A non-transitory computer readable storage device including executable instructions for ingress detection and localization within a customer premises equipment network, which when executed by a processor, is operable to:

collect data associated with customer premises equipment activities within a customer premises equipment network;

detect, with a machine learning engine, customer premises equipment nodes within the collected data that are generating ingress data above a first predetermined level;

identify locations of ingress data generation within the detected customer premises equipment nodes using a smart amplifier radio frequency tray, wherein the smart amplifier radio frequency tray is connected between the customer premises equipment nodes and a network node that connects the customer premises equipment nodes to a cable modem termination system;

detect, by the machine learning engine, identified ingress generation locations generating ingress data exceeding a second predetermined level; and

reduce ingress data generation from the detected identified locations.

16 . The non-transitory computer readable storage device of claim 15 , wherein the detection of locations that are generating excessive ingress is based on applying support vector machine (SVM) analysis on subgroup discovery and analytics (SCAN) results.

17 . The non-transitory computer readable storage device of claim 15 , wherein the machine learning engine is trained by rules input to the machine learning engine and by capturing and analyzing data received from the customer premises equipment network.

18 . The non-transitory computer readable storage device of claim 17 , wherein the machine learning engine is further trained by cable modem termination system data and by cable modem data.

19 . The non-transitory computer readable storage device of claim 15 , wherein the collected data includes three or more of:

command line interface data;

smart amplifier radio frequency tray data;

modem data;

node data;

rules;

frequency data;

upstream data;

downstream data;

sensor data; and

customer premises equipment data.

20 . The non-transitory computer readable storage device of claim 15 , wherein the locations of the generation of ingress is determined based on the collected data from the smart amplifier radio frequency tray.