Invention Grant
- Patent Title: Malware detection using local computational models
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Application No.: US15657379Application Date: 2017-07-24
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Publication No.: US10726128B2Publication Date: 2020-07-28
- Inventor: Sven Krasser , David Elkind , Patrick Crenshaw , Kirby James Koster
- Applicant: CrowdStrike, Inc.
- Applicant Address: US CA Irvine
- Assignee: CrowdStrike, Inc.
- Current Assignee: CrowdStrike, Inc.
- Current Assignee Address: US CA Irvine
- Agency: Lee & Hayes, P.C.
- Main IPC: G06F21/56
- IPC: G06F21/56 ; G06F21/55 ; H04L29/06 ; G06N20/20 ; G06N3/04 ; G06N3/08 ; G06N7/00

Abstract:
Example techniques herein determine that a trial data stream is associated with malware (“dirty”) using a local computational model (CM). The data stream can be represented by a feature vector. A control unit can receive a first, dirty feature vector (e.g., a false miss) and determine the local CM based on the first feature vector. The control unit can receive a trial feature vector representing the trial data stream. The control unit can determine that the trial data stream is dirty if a broad CM or the local CM determines that the trial feature vector is dirty. In some examples, the local CM can define a dirty region in a feature space. The control unit can determine the local CM based on the first feature vector and other clean or dirty feature vectors, e.g., a clean feature vector nearest to the first feature vector.
Public/Granted literature
- US20190026466A1 MALWARE DETECTION USING LOCAL COMPUTATIONAL MODELS Public/Granted day:2019-01-24
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