The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 27, 2022

Filed:

Dec. 21, 2020
Applicant:

National Taiwan University, Taipei, TW;

Inventors:

Phone Lin, Taipei, TW;

Xin-Xue Lin, Taipei, TW;

En-Hau Yeh, Taipei, TW;

Chia-Peng Lee, Taipei, TW;

Char-Dir Chung, Taipei, TW;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 12/28 (2006.01); H04L 45/74 (2022.01); H04L 43/0876 (2022.01); H04L 43/12 (2022.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
H04L 45/74 (2013.01); G06N 20/00 (2019.01); H04L 43/0876 (2013.01); H04L 43/12 (2013.01);
Abstract

An anomaly flow detection device and an anomaly flow detection method thereof are provided. The device can retrieve a plurality of training data transmitted between a monitored network and an external network, preprocess a plurality of packet headers of the pluralities of training data to obtain a plurality of training feature vectors, construct a flow recognition model with an unsupervised learning method, input the pluralities of training feature vectors to the flow recognition model to train the flow recognition model, retrieve a plurality of testing data transmitted between the monitored network and the external network, preprocess a plurality of packet headers of the pluralities of testing data to obtain a plurality of testing feature vectors, input the pluralities of testing feature vectors to the flow recognition model to identify whether the pluralities of packet headers of the pluralities of testing data are normal or abnormal, and determine the flow of the monitored network is abnormal according to the recognition result of the flow recognition model.


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