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:
Nov. 22, 2022

Filed:

Nov. 13, 2020
Applicant:

Honeywell International Inc., Morris Plains, NJ (US);

Inventors:

Amit Srivastav, Hyderabad, IN;

Rajesh Chenchu, Tirupati, IN;

Nayyar Azam Khan Rao, Bangalore, IN;

Phani Ammi Raju Pothula, Peravali, IN;

Vijayshankaran Iyer, Phoenix, AZ (US);

Assignee:

Honeywell International Inc., Charlotte, NC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 9/40 (2022.01); H04L 67/12 (2022.01); H04L 43/045 (2022.01); G06N 20/00 (2019.01); H04L 41/16 (2022.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06N 20/00 (2019.01); H04L 41/16 (2013.01); H04L 43/045 (2013.01); H04L 63/0236 (2013.01); H04L 63/1433 (2013.01); H04L 67/12 (2013.01);
Abstract

A method of monitoring network traffic of a connected vehicle. The method includes receiving network traffic information from a vehicle gateway, the network traffic information including malicious and/or benign information. The method also includes storing the network traffic information on a data server and periodically updating the network traffic information stored on the data server. The method further includes: pre-processing the network traffic information, the pre-processing the network traffic information including filtering and normalizing the network traffic information; generating a learning model based on the pre-processed network traffic information, the learning model being generated by an artificial intelligence learning; updating the learning model based on additional network traffic information, the additional network traffic information including real-time network data; in accordance with the updated learning model, detecting an anomaly event in the incoming network data; and generating a notification and/or blocking one or more packets associated with the incoming network data.


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