Unmanned Aircraft Vehicle (UAV) Pilot Identification Using Machine Learning

Running head: UAV PILOT IDENTIFICATION
1
Unmanned Aircraft Vehicle
(UAV) Pilot Identification Using
Machine Learning
Ahmed Saeed Al Shemeili
PhD. Thesis
February 2019
A thesis submitted to Khalifa University of Science and Technology in accordance with the
requirements of the degree of PhD in Engineering in the Department of (Electrical and
Computer Engineering).

UAV PILOT IDENTIFICATION 2
Unmanned Aircraft Vehicle (UAV) Pilot Identification Using Machine Learning
by
Ahmed Saeed Al Shemeili
A thesis submitted in partial fulfillment of the
requirements for the degree of
PhD in Engineering
at
Khalifa University
Thesis Committee
Dr. Abdulhadi Shoufan (Supervisor),
Khalifa University
Prof. Ernesto Damiani (Co-Supervisor)
Khalifa University
Dr. VWY (Industrial Supervisor),
EBTIC
Prof. XYZ (External Examiner &
Committee Chair),
University of TTT
Dr. MNV (Internal Examiner)
Khalifa University
February 2019

Abstract
This document focus on identification of Unmanned Aerial Vehicle using a novel
machine learning approach. In doing so, three classification machine learning models
including logistics regression, random forest and neural network are applied by using radio
controlled RC measurement features to detect rogue drones. The findings suggest that for
high altitude the proposed machine learning approach can perform effectively compared to
lower altitudes since the detection degrades with decrease in altitudes.
Indexing Terms: UAV, machine learning, random forest, ensemble, bagging, MATLAB.

Acknowledgement
Undertaking this PhD has been a major work experience change for me where I shift
my career from networking in cyber security and artificial intelligence and this was possible
with the support I received from many people and Khalifa University.
I would like first to thank my supervisor Dr. Abdulhadi Shoufan for all the support he
gave me and the continuous feedback, which helps during my PhD study.
Many thanks to Prof. Ernest Damiani for his support and guidance to ensure that my
PhD study will be successful and of benefit to our society
I gratefully acknowledge the funding received towards my PhD from Khalifa
University. Thanks to Prof. Mahmoud Al Qutairi for supporting my study at Khalifa
University. Thanks to my Father, Mom, Brothers and sisters
Thank you my Wife and kids

Declaration and Copyright
Declaration
I declare that the work in this thesis was carried out in accordance with the regulations
of Khalifa University of Science and Technology. The work is entirely my own except where
indicated by special reference in the text. Any views expressed in the thesis are those of the
author and in no way represent those of Khalifa University of Science and Technology. No
part of the thesis has been presented to any other university for any degree.
Author Name: Ahmed Saeed Al Shemeili
Author Signature:
Date: 24-02-2019
Copyright ©
No part of this thesis may be reproduced, stored in a retrieval system, or transmitted,
in any form or by any means, electronic, mechanical, photocopying, recording, scanning or
otherwise, without prior written permission of the author. The thesis may be made available
for consultation in Khalifa University of Science and Technology Library and for inter-
library lending for use in another library and may be copied in full or in part for any bona fide
library or research worker, on the understanding that users are made aware of their
obligations under copyright, i.e. that no quotation and no information derived from it may be
published without the author's prior consent.

Table of Contents
Abstract.......................................................................................................................................i
Acknowledgement....................................................................................................................iii
Declaration and Copyright..........................................................................................................i
Declaration.............................................................................................................................i
Copyright ©...........................................................................................................................i
List of Figures...........................................................................................................................iii
List of tables..............................................................................................................................iv
List of Abbreviations..................................................................................................................v
CHAPTER 1...............................................................................................................................6
1.0 Introduction....................................................................................................................6
The Aim of the Project.......................................................................................................12
Project Objective................................................................................................................12
CHAPTER 2.............................................................................................................................12
Related Works....................................................................................................................12
Fundamental technological framework of UAV..........................................................12
Drones and cyber security..............................................................................................16
CHAPTER 3.............................................................................................................................17
3.0 Research Methodology.................................................................................................17
Techniques to be used.....................................................................................................17
Data Collection................................................................................................................18
Tools and Applications...................................................................................................19
CHAPTER 4.............................................................................................................................21
Preliminary Results........................................................................................................21
Five Pilots Dataset...........................................................................................................21
CHAPTER 5.............................................................................................................................29

Conclusion...........................................................................................................................29
Moving Forward.............................................................................................................30
Project Time Plan...............................................................................................................30

List of Figures
Figure 1: Navigation system architecture ..................................................14
Figure 2: UAV directions.......................................................................15
Figure 3: General architecture of a drone including the ground control station....16
Fig 4: UAV detection .............................................................................17
Figure 5: Cross validation........................................................................20
Figure 6: Performance of all classifiers on the same dataset..............................22
Figure 7. Features test accuracy with different crosses starting from 50% up to
98%....................................................................................................................................24
Figure8: Single features output compared to all features output accuracy............25
Figure 9: All Data Vs. RC Data Classification Performance..............................26
Figure 10: Pilot performance....................................................................27
Figure 11: project timeline plan................................................................31
List of tables
Table 1: Pilots data...............................................................................20
Table 2: Different classifiers accuracy test..................................................21
Table 3: Cross validation test..................................................................23
Table 4: Single feature test.....................................................................24
Table 5. Effectiveness order of the features.................................................25
Table 6: All data vs RC data performance.................................................26
Table 7: Pilot performance.....................................................................27
Table 8: Features accuracy test...............................................................29