- Patent Title: Deployment of machine learning models for discernment of threats
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Application No.: US16425662Application Date: 2019-05-29
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Publication No.: US10657258B2Publication Date: 2020-05-19
- Inventor: Kristopher William Harms , Renee Song , Raj Rajamani , Braden Rusell , Yoojin Sohn , Kiefer Ipsen
- Applicant: Cylance Inc.
- Applicant Address: US CA Irvine
- Assignee: Cylance Inc.
- Current Assignee: Cylance Inc.
- Current Assignee Address: US CA Irvine
- Agency: Jones Day
- Main IPC: G06F21/56
- IPC: G06F21/56 ; G06N20/00 ; G06F21/51 ; H04L29/06 ; G06F3/048

Abstract:
A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.
Public/Granted literature
- US20190294797A1 Deployment of Machine Learning Models for Discernment of Threats Public/Granted day:2019-09-26
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