Unmanned Aircraft Vehicle (UAV) Pilot Identification Using Machine Learning

Unmanned Aircraft Vehicle (UAV)
Pilot Identification Using Machine
Learning

Contents
1.0 Chapter - 1................................................................................................... 5
1.1 Introduction.................................................................................................. 5
1.2 Aims and Objective....................................................................................... 9
2.0 Chapter - 2................................................................................................. 10
2.1 Working of UAVs......................................................................................... 10
2.2 Security aspects in UAVs............................................................................ 13
2.3 Safety Aspects - Cyber Security Approaches..............................................18
2.4 Thumbprint and biometric security devices...............................................29
2.5 Machine Learning Approaches....................................................................36
3.0 Chapter - 3................................................................................................. 77
3.1 Classifications of UAV.................................................................................77
3.2 Architecture of UAV.................................................................................... 77
3.3 Cyber protection of .................................................................................UAV
82
3.4 RF............................................................................................................... 88
3.5 Scientific practices..................................................................................... 89
3.6 Machine Learning....................................................................................... 90
3.7 Machine Learning Techniques....................................................................90
Supervised learning.......................................................................................... 91
Unsupervised Learning..................................................................................... 91
3.8 Class Imbalance in Machine Learning.........................................................92
Detect a “naive behaviour”..............................................................................92
Confusion matrix, precision, recall and F1........................................................92
3.9 Critical Analysis.......................................................................................... 94
3.10 A number of classifiers for further analysis..............................................95
4.0 Chapter - 4................................................................................................. 96
4.1 Data Sets Used......................................................................................... 109
4.2 Results...................................................................................................... 110
4.3 RCTriangle = 15156×5 table...................................................................119
4.4 RCVertical = 10470×5 table.....................................................................124
4.5 CompleteRandom = 54881×21 table.......................................................130
5.0 Chapter - 5............................................................................................... 154
5.1 Conclusion............................................................................................. 154
References......................................................................................................... 156

List of Figures
Figure 1. Typical UAV Communication Scenario27
Figure 2. Simple UAV block diagram31
Figure 3. UAV Communication Model31
Figure 4. UAV System Cyber-Security Threat Model33
Figure 5. Regression of Logistic42
Figure 6. Logarithmic Transformation of the Sigmoid Function43
Figure 7. Cost Function Part 144
Figure 8. Cost Function Part 245
Figure 9. Simplified Cost Function45
Figure 10. Maximum Likelihood Explanation46
Figure 11. Gradient48
Figure 12. Decision Boundary from Random Forests (as more trees are added)50
Figure 13. Random Forests Perform Better Than Single Decision Trees51
Figure 14. Boundary from an Unpruned Tree52
Figure 15. Unpruned Trees with No Overfitting53
Figure 16. Interpret a Random Tree56
Figure 17. Tree Based Modeling58
Figure 18. Tree Recognizes59
Figure 19. Decision Tree60
Figure 20. Reversion Trees and Division Trees61
Figure 21. Clusters65
Figure 22. Decision Tree66
Figure 23. Pruning of Tree66
Figure 24. HackRF One68
Figure 25. FCC ID on 3DR Solo Drone Transmitter69
Figure 26. Working Frequency of DJI Phantom 2 Transmitter70
Figure 27. Wifi Analyzer71
Figure 28. GHZ Spectrum Prior Power on of 3DR Transmitter72
Figure 29. 3 DR Solo Channel73
Figure 30. Architecture of UAV78

Figure 31. Directions of UAV79
Figure 32. IMU80
Figure 33. Connected Mechanisms81
Figure 34. Chances, Applications and Dares of UAV82
Figure 35. Cyber Protection of UAV83
Figure 36. Cyber Security Challenges84
Figure 37. Pressures Against UAV85
Figure 38. Practicing an Group86
Figure 39. Random Forest89
Figure 40. Cross Authentication99
Figure 41. Presentation of all Classifiers on Similar Dataset101
Figure 42. Structures Test Precision with Various Crosses Beginning from 50% to 98%.103
Figure 43. Individual Structures Result Compared to all Structured Result Precision105
Figure 44. Data vs RC Data Classification Presentation106
Figure 45. Presentation of aviator108
Figure 46.a. Predictor importance112
Figure 47.b. Predictor importance113
Figure 48.c. Predictor importance estimates on RCVertical timates on RCTriangle timates on
RCTriangle113
Figure 49.a. Predictor importance estimates on complete vertical146
Figure 50.b. Predictor importance estimates on complete triangle146
Figure 51.c. Predictor importance estimates on complete random147
Figure 52.a. Predictor importance estimates on complete vertical147
Figure 53.b. Predictor importance estimates on complete triangle148
Figure 54.c. Predictor importance estimates on complete random148
Figure 55.a. Predictor importance149
Figure 56.b. Predictor importance150
Figure 57. Predictor importance estimates on RCVertical timates on RCTriangle timates on
RCTriangle150
Figure 58.a. Evaluation of yaw single152
Figure 59.b. Evaluation of roll single153
Figure 60.c. Evaluation of pitch single153
Figure 61.d. Evaluation of thrust signal154

List of Tables
Table 1. Risk Evaluation Grid35
Table 2. Analysis Summary36
Table 3. Detection Results77
Table 4. Detection Rate77
Table 5. Comparison of Different Group Algorithm88
Table 6. Pilot’s Details97
Table 7. Various Classifiers Correctness Test99
Table 8. Cross Authentication Test101
Table 9. Individual Feature Test103
Table 10. Efficacy Arrangement of the Structures105
Table 11. Data vs. RC Data Presentation106
Table 12. Aviator presentation107
Table 13. Structures precision test109
Table 14. Specific Observations111
Table 15.Dataset 1112

1.0 Chapter - 1
There are 5 chapters in this thesis. Chapter 1 covered the introductory details about the
drones, the applications of drones and the Aims and Objectives of the research. The technical
details about the UAVs, the security aspects of UAVs, The safety aspects of UAVs, The
cyber security approaches and machine learning approaches in UAVs are covered in Chapter-
2. Chapter 3 covers further details about the UAVs like classification, Architecture, Cyber
protection, RF, Scientific practices in UAVs. Machine learning techniques like supervised
learning and unsupervised learning, class imbalance of machine learning details are covered.
Critical analysis and number of classifiers are also covered in chapter-3. Chapter-4 starts with
the data set details , how the data analysis done, what results are obtained initially and how
the improved results are obtained. Critical analysis is done on the results and very specific
details are explained in the critical analysis. Chapter-5 is a conclusion chapter and the details
inferences of the data analyses, data results and the inferences from the data analysis is done.
Upto what lever the objectives and aims are met also discussed.
1.1 Introduction
Drone - a powered, aerial vehicle that utilizes aerodynamic services that gives automobile
boosting, and glide originally with a lethal or non-lethal payload as well as they are
unmanned, find a plethora of services. These drones are divided by several kinds depends
upon their usage. Most of them are remote-controlled, autonomous or semi-autonomous
while some of them can move, hover or perch with a minimum required human intervention.
Recent technology allows the drone to land autonomously and are easily transported back-
pack or by hand. The classification of drones can be based on a wide range of applications.
The various characteristics that can be used as criteria for classification are:
1. platform
2. altitude
3. spectral resolution
4. flexibility
5. remote sensing missions
6. quality
7. economic cost
8. spatial accuracy
9. target
10. update rate
11. coverage
12. maneuverability
13. endurance
14. weather and wind

15. power
16. size
17. weight
18. operating range
19. certification
20. flight type
21. payload

Drones are considered to be a major leap in surveying and engineering measurement
technology of the 21st century. This most intriguing electronic technology has demonstrated
enormous capabilities for usage in a wide spectrum of applications. Hence many researchers
and manufacturers are willing to build new drones to support daily life. But the great obstacle
is the lack of a systematic categorization which allows operators to choose a device that
fulfils their own requirements. In this paper, the five potential sectors of drones are
recognized as: emergency, earth observation, infrastructure monitoring and inspection,
environment, defense and security. All existing drones are classified under the above-
mentioned sectors or groups.
Emergency
Since drones are unmanned vehicles, they can be a great resource during emergency
situations. Drones can assist firefighter in safely accessing buildings on fire, and perform
navigation to coordinate a not so dangerous traceable flight path through the fire. They can be
used for monitoring forest fire and automatic route flights, too.
During rescue missions drones can be used for surveillance, research, monitoring, tracking,
collecting weather (flood, fire) information, and imaging, flood mapping, assessing roads,
monitoring sea levels etc. and sending the relevant data to the ground station.
Cargo drones play a key role in performing delivery services related to courier, retail and
hospitality sectors. Ambulance drones can greatly decrease the response time leading to an
increase in survival rate.
Infrastructure Monitoring and Inspection
Drones are a very good candidate for high accuracy surveys of a vast area. They can be used
to gain a big picture of a development project, chemical or oil spill monitoring, quick
topographical survey. In the real estate sector drones can aid in gathering aerial data, site
monitoring and remote viewing of landscape both by the developers and insurance agents.
Drones can greatly assist in real-time surveillance of logistics, power lines, power plants,
thermal imaging and 3D laser scanning equipment. These devices save a lot of time and
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