US 11,695,792 B2
Leveraging synthetic traffic data samples for flow classifier training
Blake Harrell Anderson, Chapel Hill, NC (US); and David McGrew, Poolesville, MD (US)
Assigned to Cisco Technology, Inc., San Jose, CA (US)
Filed by Cisco Technology, Inc., San Jose, CA (US)
Filed on Jan. 6, 2021, as Appl. No. 17/142,533.
Application 17/142,533 is a continuation of application No. 15/364,933, filed on Nov. 30, 2016, granted, now 10,904,275.
Prior Publication US 2021/0160268 A1, May 27, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 29/06 (2006.01); G06N 20/00 (2019.01); H04L 12/24 (2006.01); H04L 12/851 (2013.01); H04L 9/40 (2022.01); H04L 41/16 (2022.01); H04L 47/2441 (2022.01)
CPC H04L 63/1425 (2013.01) [G06N 20/00 (2019.01); H04L 41/16 (2013.01); H04L 47/2441 (2013.01); H04L 63/1458 (2013.01); H04L 63/306 (2013.01); H04L 2463/141 (2013.01); H04L 2463/144 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, at a device in a network, traffic data regarding a plurality of observed traffic flows;
determining, by the device, one or more environment parameters associated with a targeted deployment environment in which a machine learning-based traffic classifier is to be deployed, wherein the targeted deployment environment is different than the network in which the traffic data was received;
modifying, by the device, one or more samples of the plurality of observed traffic flows from the traffic data to match traffic characteristics of the targeted deployment environment based on the one or more environment parameters associated with the targeted deployment environment;
creating, by the device, synthetic traffic data that resembles actual traffic data expected in the targeted deployment environment based on the one or more modified samples, wherein the synthetic traffic data is not actually observed in the network; and
training, by the device, the machine learning-based traffic classifier using the synthetic traffic data for deployment in the targeted deployment environment.