Invention Grant
- Patent Title: Scalable training of random forests for high precise malware detection
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Application No.: US15722412Application Date: 2017-10-02
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Publication No.: US10885469B2Publication Date: 2021-01-05
- Inventor: Jan Brabec , Lukas Machlica
- Applicant: Cisco Technology, Inc.
- Applicant Address: US CA San Jose
- Assignee: Cisco Technology, Inc.
- Current Assignee: Cisco Technology, Inc.
- Current Assignee Address: US CA San Jose
- Agency: Behmke Innovation Group LLC
- Agent Kenneth J. Heywood; Jonathon P. Western
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F21/56 ; G06N5/04 ; G06K9/62 ; G06N5/02 ; H04L29/06 ; G06N5/00 ; G06N20/20

Abstract:
In one embodiment, a device trains a machine learning-based malware classifier using a first randomly selected subset of samples from a training dataset. The classifier comprises a random decision forest. The device identifies, using at least a portion of the training dataset as input to the malware classifier, a set of misclassified samples from the training dataset that the malware classifier misclassifies. The device retrains the malware classifier using a second randomly selected subset of samples from the training dataset and the identified set of misclassified samples. The device adjusts prediction labels of individual leaves of the random decision forest of the retrained malware classifier based in part on decision changes in the forest that result from assessing the entire training dataset with the classifier. The device sends the malware classifier with the adjusted prediction labels for deployment into a network.
Public/Granted literature
- US20190102337A1 SCALABLE TRAINING OF RANDOM FORESTS FOR HIGH PRECISE MALWARE DETECTION Public/Granted day:2019-04-04
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