US 12,407,717 B2
Machine learning architecture for detecting malicious files using stream of data
William Redington Hewlett, II, Mountain View, CA (US); Sujit Rokka Chhetri, Santa Clara, CA (US); Brody James Kutt, Santa Clara, CA (US); Shan Huang, San Jose, CA (US); Nandini Ramanan, Sunnyvale, CA (US); Sheng Yang, Santa Clara, CA (US); and Min Du, Santa Clara, CA (US)
Assigned to Palo Alto Networks, Inc., Santa Clara, CA (US)
Filed by Palo Alto Networks, Inc., Santa Clara, CA (US)
Filed on Jan. 31, 2023, as Appl. No. 18/104,137.
Prior Publication US 2024/0259420 A1, Aug. 1, 2024
Int. Cl. H04L 29/06 (2006.01); H04L 9/40 (2022.01); H04L 41/16 (2022.01)
CPC H04L 63/145 (2013.01) [H04L 41/16 (2013.01)] 27 Claims
OG exemplary drawing
 
1. A system for performing classification at an edge device, comprising:
one or more processors configured to:
obtain a stream of a file at the edge device;
align a predetermined amount of data in chunks associated with the stream of the file, wherein aligning the predetermined amount of data in chunks associated with the stream of the file comprises:
determining an nth file segment based at least in part on associating a predetermined amount of an ith chunk with a predetermined amount of a jth chunk, wherein i and j are positive integers, and j is greater than i;
process a plurality of aligned chunks associated with the stream of the file using a machine learning model; and
classify, at the edge device, the file based at least in part on a classification of the plurality of aligned chunks, wherein the file is classified based at least in part on a classification of the nth file segment; and
a memory coupled to the one or more processors and configured to provide the one or more processors with instructions.