Projects:2018s1-116 Data Analytics - Revision history

A1687697 at 16:24, 21 October 2018

2018-10-21T16:24:16Z

A1687697 at 16:18, 21 October 2018

2018-10-21T16:18:38Z

A1687697 at 16:15, 21 October 2018

2018-10-21T16:15:21Z

A1687697 at 12:32, 12 April 2018

2018-04-12T12:32:08Z

A1687697: Created page

2018-04-12T12:31:10Z

Created page

New page

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= Project Team =
*Liam Flaherty

= Project Supervisors =
*Dr. Hong Gunn Chew
*Prof. Lang White
*Boeing Defence Australia

= Introduction =
ADS-B (Automatic Dependent Surveillance Broadcast) is an increasingly used system for tracking aircraft and can be used for the purpose of air traffic management. It is a system by which aircraft transmit information regarding their identification and aircraft state, including positional and speed information. This information can be used to make informed decisions related to aircraft control to increase efficiency and safety of air travel. If an aircraft is exhibiting unexpected or dangerous behaviour, operators can intervene. Examples of this behaviour include malicious intent, aircraft malfunction, and pilot error, all of which can result in an aircraft deviating from usual operation and posing a potential risk.

In addition, the transmission of ADS-B data is voluntary and unencrypted. This makes it especially vulnerable to spoofing attacks and tampering. In this instance, a spoofing attack consists of injecting false data into the system in order to degrade the system's performance. An example of such an attack is fabricating a bogus aircraft to interfere with the flights of other aircraft, despite its lack of existence. This is possible due to the nature of ADS-B being voluntary, unencrypted, and lacking in authentication. This example could engage collision avoidance algorithms in order to save the real aircraft, but would just lead to flight performance degradation. Once this data is injected into the system, it is difficult to identify bogus data.

These two issues are the focus of this project. This project aims to develop a machine learning system which can automatically determine anomalies in air tracks, where an anomaly falls under one of the above definitions. The approach for the project is to take a number of different models which learn different pieces of information about legitimate aircraft in order to extract possible anomalous features from the current airspace. These conclusions can be used to assist operators in monitoring the airspace by highlighting potential anomalies or verifying legitimate aircraft and removing them from consideration.

@@ Line 78: / Line 78: @@
 The Dense (Distance) format follows the same principle as the Dense (Time) format, however rather than encoding latitude and longitude, it encodes the distance and angle from the airport in the z axis.
-[[File:data-graphic.png |frame|center| Graphic data representation. 1st row: arrivals, 2nd row: departures, 3rd row: passing through. The red channel encodes altitude, the green channel encodes time, latitude and longitude are encoded in x and y coordinates.]]
+[[File:Data-graphic.png |frame|center| Graphic data representation. 1st row: arrivals, 2nd row: departures, 3rd row: passing through. The red channel encodes altitude, the green channel encodes time, latitude and longitude are encoded in x and y coordinates.]]
 == CNN Training ==

@@ Line 46: / Line 46: @@
 computer system
-[[File:System_v3 |frame|center| Complete system diagram]]
+[[File:System_v3.png |frame|center| Complete system diagram]]
 = Model Development =
@@ Line 78: / Line 78: @@
 The Dense (Distance) format follows the same principle as the Dense (Time) format, however rather than encoding latitude and longitude, it encodes the distance and angle from the airport in the z axis.
-[[File:data-graphic |frame|center| Graphic data representation. 1st row: arrivals, 2nd row: departures, 3rd row: passing through. The red channel encodes altitude, the green channel encodes time, latitude and longitude are encoded in x and y coordinates.]]
+[[File:data-graphic.png |frame|center| Graphic data representation. 1st row: arrivals, 2nd row: departures, 3rd row: passing through. The red channel encodes altitude, the green channel encodes time, latitude and longitude are encoded in x and y coordinates.]]
 == CNN Training ==

@@ Line 10: / Line 10: @@
 *Dr. Hong Gunn Chew
 *Prof. Lang White
-*Boeing Defence Australia
+*Ms. Natalie Harris (Boeing Defence Australia)
@@ Line 19: / Line 19: @@
 These two issues are the focus of this project. This project aims to develop a machine learning system which can automatically determine anomalies in air tracks, where an anomaly falls under one of the above definitions. The approach for the project is to take a number of different models which learn different pieces of information about legitimate aircraft in order to extract possible anomalous features from the current airspace. These conclusions can be used to assist operators in monitoring the airspace by highlighting potential anomalies or verifying legitimate aircraft and removing them from consideration.

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	= Project Team =		= Project Team =