Invention Grant
- Patent Title: Using machine learning to detect malicious upload activity
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Application No.: US18109772Application Date: 2023-02-14
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Publication No.: US11936670B2Publication Date: 2024-03-19
- Inventor: Syed Ali Bilgrami
- Applicant: Sequoia Benefits and Insurance Services, LLC
- Applicant Address: US CA San Mateo
- Assignee: Sequoia Benefits and Insurance Services, LLC
- Current Assignee: Sequoia Benefits and Insurance Services, LLC
- Current Assignee Address: US CA San Mateo
- Agency: Lowenstein Sandler LLP
- Main IPC: H04L9/40
- IPC: H04L9/40 ; G06F18/23 ; G06F18/24 ; G06N3/08 ; G06N20/00

Abstract:
A method for training a machine learning model using information pertaining to characteristics of upload activity performed at one or more client devices includes generating first training input including (i) information identifying, for each of a plurality of application categories, data categories pertaining to first amounts of data uploaded from the client device during a specified time interval. The method includes generating a first target output that indicates whether the data categories corresponding to the first amounts of data correspond to malicious or non-malicious upload activity. The method includes providing the training data to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.
Public/Granted literature
- US20230199008A1 USING MACHINE LEARNING TO DETECT MALICIOUS UPLOAD ACTIVITY Public/Granted day:2023-06-22
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