US 12,483,484 B2
Device identification method, apparatus, and system
Weiwang Xu, Nanjing (CN); Li Xue, Nanjing (CN); Haonan Ye, Nanjing (CN); Jian Cheng, Nanjing (CN); and Liang Zhang, Nanjing (CN)
Assigned to HUAWEI TECHNOLOGIES CO., LTD., Shenzhen (CN)
Filed by Huawei Technologies Co., Ltd., Shenzhen (CN)
Filed on Apr. 21, 2023, as Appl. No. 18/305,115.
Application 18/305,115 is a continuation of application No. PCT/CN2021/124990, filed on Oct. 20, 2021.
Claims priority of application No. 202011145036.0 (CN), filed on Oct. 23, 2020; and application No. 202110221855.7 (CN), filed on Feb. 27, 2021.
Prior Publication US 2023/0261948 A1, Aug. 17, 2023
Int. Cl. G06F 15/173 (2006.01); H04L 41/147 (2022.01); H04L 43/04 (2022.01); H04L 61/5007 (2022.01); H04L 101/668 (2022.01)
CPC H04L 41/147 (2013.01) [H04L 43/04 (2013.01); H04L 61/5007 (2022.05); H04L 2101/668 (2022.05)] 13 Claims
OG exemplary drawing
 
1. A device identification method, wherein the method comprises:
determining a network traffic feature of a to-be-identified device based on a first dataset, wherein the first dataset comprises a plurality of pieces of first data, wherein each piece of first data comprises a data amount of a data packet that is related to the to-be-identified device and that is collected within a first periodicity, and wherein the data packet is a heartbeat packet having a packet length less than a target length threshold; and
determining a device type of the to-be-identified device based on a device identification model and the network traffic feature of the to-be-identified device by inputting the network traffic feature of the to-be-identified device to the device identification model;
wherein the device identification model is a machine learning model obtained through training based on network traffic features of a plurality of known devices of a known device type.