Michelle Rivers

US Patent:

20180262531, Sep 13, 2018

Filed:

Mar 9, 2017

Appl. No.:

15/453991

Inventors:

- Armonk NY, US
Michael E. Nidd - Zurich, CH
Michelle Rivers - Marietta GA, US
George E. Stark - Lakeway TX, US
Srinivas B. Tummalapenta - Broomfield CO, US
Dorothea Wiesmann - Oberrieden, CH

International Classification:

H04L 29/06
G06N 99/00

Abstract:

Training a random forest model to relate settings of a network security device to undesirable behavior of the network security device is provided. A determination of a corresponding set of settings associated with each region of lowest incident probability is made using a random forest. The plurality of identified desired settings are presented as options for changing the network security device from the as-is settings to the identified desired settings. A choice is received from the plurality of options. The choice informs the random forest model. The random forest model ranks for a new problematic network security device the plurality of options for changing the new problematic network security device from as-is settings to desired settings by aggregating an identified cost of individual configuration changes, thereby identifying a most cost-effective setting for the network security device to achieve a desired output of the network security device.

US Patent:

20180174069, Jun 21, 2018

Filed:

Dec 15, 2016

Appl. No.:

15/381096

Inventors:

- Armonk NY, US
Michael E. Nidd - Zurich, CH
Michelle Rivers - Marietta GA, US
George E. Stark - Lakeway TX, US
Srinivas B. Tummalapenta - Broomfield CO, US
Dorothea Wiesmann - Oberrieden, CH

International Classification:

G06N 99/00
H04L 12/751

Abstract:

A computer-implemented method of increasing reliability of an information technology environment comprising a plurality of hardware devices. Training data is received and a random forest is built from the training data using machine learning. A particular hardware device in the plurality of hardware devices is determined to be strange. Strange is defined as the particular hardware device having a proximity value lower than a predetermined threshold value for the random forest. A preventative action is determined to lower a risk of failure of the particular hardware device. The preventative action is reported. Reporting includes at least one of displaying a report on a display device, printing the report onto paper, and storing the report in a non-transitory computer recordable storage medium.