Armando Chavez Rodriguez

20210320934, Oct 14, 2021

Jun 23, 2021

17/356464

- San Jose CA, US
Yonghong Huang - Hillsboro OR, US
Eric Peterson - Elizabeth CO, US
John Wagener - West Lakeland MN, US
Joanna Negrete - Santa Clara CA, US
Armando Rodriguez - Parker CO, US
Celeste Fralick - Lubbock TX, US
Sandeep Chandana - Santa Clara CA, US

H04L 29/06
G06N 20/00

Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.

20210014247, Jan 14, 2021

Jul 9, 2019

16/505969

- Santa Ckara CA, US
Yonghong Huang - Hillsboro OR, US
Eric Peterson - Elizabeth CO, US
John Wagener - West Lakeland MN, US
Joanna Negrete - Santa Clara CA, US
Armando Rodriguez - Parker CO, US
Celeste Fralick - Lubbock TX, US
Sandeep Chandana - Santa Clara CA, US

H04L 29/06
G06N 20/00

Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.