US 12,255,905 B2
Machine learning malware classifications using behavioral artifacts
Vitaly Zaytsev, Beaverton, OR (US); Brett Meyer, Alpharetta, GA (US); and Joel Robert Spurlock, Portland, OR (US)
Assigned to CrowdStrike, Inc., Sunnyvale, CA (US)
Filed by CrowdStrike, Inc., Sunnyvale, CA (US)
Filed on Apr. 20, 2022, as Appl. No. 17/725,352.
Prior Publication US 2023/0344843 A1, Oct. 26, 2023
Int. Cl. H04L 9/40 (2022.01)
CPC H04L 63/1425 (2013.01) [H04L 63/145 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
programming instructions configured to be executed by the one or more processors to perform operations comprising:
receiving, from one or more monitored devices, events data for training data;
presenting false-negative data of the events data based on a labeled indicator of the false-negative data incorrectly labeled as false-negative or false-positive;
generating labeled data by relabeling one or more processes in the false-negative data;
storing the labeled data as the training data;
determining that the events data is associated with a malicious process;
determining to map individual events from the events data onto a behavioral activity pattern;
aggregating multiple events of the individual events into a single artifact, the multiple events produced by the malicious process;
generating a malware classifier based at least in part on extracting behavioral artifacts including the single artifact from the behavioral activity pattern and building a feature vector associated with the extracted behavior artifacts used for the malware classifier;
transmitting, to the one or more monitored devices, the malware classifier; and
receiving, from the one or more monitored devices, additional events data for additional training data.