US 12,284,094 B2
Utilizing machine learning models for network traffic categorization
Ajit Krishna Patankar, Fremont, CA (US); Kaushik Adesh Agrawal, Chelmsford, MA (US); Kihwan Han, Pleasanton, CA (US); Monimoy Deb Purkayastha, Bangalore (IN); Patrick John Melampy, Dunstable, MA (US); and Patrick Timmons, Natick, MA (US)
Assigned to Juniper Networks, Inc., Sunnyvale, CA (US)
Filed by Juniper Networks, Inc., Sunnyvale, CA (US)
Filed on Dec. 28, 2022, as Appl. No. 18/147,489.
Prior Publication US 2024/0223478 A1, Jul. 4, 2024
Int. Cl. H04L 43/04 (2022.01); H04L 41/16 (2022.01); H04L 43/062 (2022.01); H04L 43/0882 (2022.01); H04L 47/27 (2022.01); H04N 21/643 (2011.01)
CPC H04L 43/04 (2013.01) [H04L 41/16 (2013.01); H04L 43/062 (2013.01); H04L 43/0882 (2013.01); H04L 47/27 (2013.01); H04N 21/643 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a device, network traffic data that includes network traffic packet sizes;
transforming, by the device, the network traffic data into transformed data;
processing, by the device, the transformed data, with a machine learning model, to generate an embedding;
obtaining, by the device, a similarity metric for the embedding;
creating, by the device, a graph with nodes and edges based on the embedding and the similarity metric;
processing, by the device, the graph, with a community detection model, to identify network traffic categories for the network traffic data,
wherein processing the graph to identify the network traffic categories for the network traffic data comprises:
processing the graph to determine a quantity of the network traffic categories automatically from the network traffic data and without prior selection of the quantity of the network traffic categories; and
performing, by the device, one or more actions based on the network traffic categories.