Invention Grant
- Patent Title: Deriving optimal actions from a random forest model
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Application No.: US15453991Application Date: 2017-03-09
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Publication No.: US10348768B2Publication Date: 2019-07-09
- Inventor: Rhonda L. Childress , Michael E. Nidd , Michelle Rivers , George E. Stark , Srinivas B. Tummalapenta , Dorothea Wiesmann
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Yee & Associates, P.C.
- Agent Jeffrey LaBaw
- Main IPC: G06F1/00
- IPC: G06F1/00 ; H04L29/06 ; G06N20/00

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.
Public/Granted literature
- US20180262531A1 Deriving Optimal Actions from a Random Forest Model Public/Granted day:2018-09-13
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