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:
Jul. 20, 2021

Filed:

Aug. 02, 2018
Applicant:

B. G. Negev Technologies and Applications Ltd., AT Ben-gurion University, Beer Sheva, IL;

Inventors:

Asaf Shabtai, Hulda, IL;

Idan Habler, Jerusalem, IL;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 11/00 (2006.01); G06F 21/56 (2013.01); G08G 5/00 (2006.01); G06N 20/00 (2019.01); G06F 21/55 (2013.01); G06N 3/04 (2006.01); G06N 7/00 (2006.01); G06F 21/64 (2013.01); G06N 3/08 (2006.01); G06N 20/20 (2019.01); G06N 5/00 (2006.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G06F 21/566 (2013.01); G06F 11/00 (2013.01); G06F 21/554 (2013.01); G06F 21/64 (2013.01); G06N 3/0427 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G08G 5/003 (2013.01); G08G 5/0004 (2013.01); G08G 5/0008 (2013.01); G08G 5/0013 (2013.01); G08G 5/0021 (2013.01); G08G 5/0056 (2013.01); G08G 5/0095 (2013.01); G06F 2221/034 (2013.01); G06N 5/003 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
Abstract

A method for detecting anomalous ADS-B messages in airplanes and air-traffic control system, comprising: extracting features from application level data, which is information broadcasted in said ADS-B messages, contextual data and flight plans; analyzing said extracted features and computing relative measures of a flight based on said extracted features; training a machine learning model to represent a benign ADS-B messages; applying said machine learning model on said extracted features thereby deriving a reputation score for said ADS-B message; issuing a decision based on said score, thereby recognizing an attack and issuing an alarm regarded said recognized attack.


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