126,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Vehicular networks were first developed to ensure safe driving and to extend the Internet to the road. However, we can now see that the ability of vehicles to engage in cyber-activity may result in tracking and privacy violations through the interception of messages, which are frequently exchanged on road.
This book serves as a guide for students, developers and researchers who are interested in vehicular networks and the associated security and privacy issues. It facilitates the understanding of the technologies used and their various types, highlighting the importance of privacy and security issues and the direct impact they have on the safety of their users. It also explains various solutions and proposals to protect location and identity privacy, including two anonymous authentication methods that preserve identity privacy and a total of five schemes that preserve location privacy in the vehicular ad hoc networks and the cloud-enabled internet of vehicles, respectively.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 350
Veröffentlichungsjahr: 2022
Ähnliche
Contents
Cover
Title Page
Copyright
Preface
List of Acronyms
Introduction
Chapter 1. Vehicular Networks
1.1. Introduction
1.2. Motivation by numbers
1.3. Evolution
1.4. Architecture
1.5. Characteristics
1.6. Technical challenges and issues
1.7. Wireless technology
1.8. Standards
1.9. Types
1.10. Test beds and real implementations
1.11. Services and applications
1.12. Public opinion
1.13. Conclusion
Chapter 2. Privacy and Security in Vehicular Networks
2.1. Introduction
2.2. Privacy issue in vehicular networks
2.3. State-of-the-art location privacy-preserving solutions
2.4. Authentication issues in vehicular networks
2.5. Identity privacy preservation authentication solutions: state of the art
2.6. Conclusion
Chapter 3. Security and Privacy Evaluation Methodology
3.1. Introduction
3.2. Evaluation methodology
3.3. Conclusion
Chapter 4. The Attacker Model
4.1. Introduction
4.2. Security objectives
4.3. Security challenges
4.4. Security attacker
4.5. Conclusion
Chapter 5. Privacy-preserving Authentication in Cloud-enabled Vehicle Data Named Networks (CVDNN) for Resources Sharing
5.1. Introduction
5.2. Background
5.3. System description
5.4. Forming cloud-enabled vehicle data named networks
5.5. Migrating the local cloud virtual machine to the central cloud
5.6. Privacy and authentication when using/providing CVDNN services
5.7. The privacy in CVDNN
5.8. Discussion and analysis
5.9. Conclusion
Chapter 6. Privacy-preserving Authentication Scheme for On-road On-demand Refilling of Pseudonym in VANET
6.1. Introduction
6.2. Network model and system functionality
6.3. Proposed scheme
6.4. Analysis and discussion
6.5. Conclusion
Chapter 7. Preserving the Location Privacy of Vehicular Ad hoc Network Users
7.1. Introduction
7.2. Adversary model
7.3. Proposed camouflage-based location privacy-preserving scheme
7.4. Proposed hybrid pseudonym change strategy
7.5. Conclusion
Chapter 8. Preserving the Location Privacy of Internet of Vehicles Users
8.1. Introduction
8.2. CE-IoV
8.3. Privacy challenges
8.4. Attacker model
8.5. CLPPS: cooperative-based location privacy-preserving scheme for Internet of vehicles
8.6. CSLPPS: concerted silence-based location privacy-preserving scheme for Internet of vehicles
8.7. Obfuscation-based location privacy-preserving scheme in cloud-enabled Internet of vehicles
8.8. Conclusion
Chapter 9. Blockchain-based Privacy-aware Pseudonym Management Framework for Vehicular Networks
9.1. Introduction
9.2. Background
9.3. Related works
9.4. Key concepts
9.5. Proposed solution
9.6. Analysis
9.7. Comparative study
9.8. Conclusion
Conclusion
References
Index
List of Illustrations
Chapter 1
Figure 1.1.
Autonomous vehicles
Figure 1.2.
IEEE WAVE standards (Sjöberg 2011)
Figure 1.3.
A simplified WAVE standard view (Rawashdeh and Mahmud 2011)
Figure 1.4.
ETSI TC ITS protocol stack (Sjöberg 2011)
Figure 1.5.
Vehicular networks.
Figure 1.6.
Automated driving levels (Litman 2018)
Chapter 2
Figure 2.1.
Vehicle tracked successfully even with a pseudonym change.
Figure 2.2.
Vehicle tracked successfully when no pseudonym update is done.
Figure 2.3.
Privacy preservation methods when using safety applications
Chapter 3
Figure 3.1.
Process of security/privacy issue resolution
Figure 3.2.
Privacy and security proof and analysis methods
Figure 3.3.
Protocol analysis process using the BAN logic.
Figure 3.4.
Example of HLSPL protocol specification (A. Team 2006a).
Figure 3.5.
Attack tree example (AND/OR nodes)
Figure 3.6.
Types of games in game theory
Chapter 4
Figure 4.1.
Cyber-security threats and solutions (Benarous et al. 2017)
Figure 4.2.
Attacker’s receivers’ dispositions to cover the observed area.
Figure 4.3.
Semantic linking attack.
Figure 4.4.
Syntactic linking attack.
Figure 4.5.
Observation mapping linking attack.
Figure 4.6.
Linking mapping attack.
Chapter 5
Figure 5.1.
Vehicular cloud types.
Figure 5.2.
Creating and/or joining the vehicular cloud.
Figure 5.3.
Illustration of cloud-enabled vehicular data named network joining and service usage process.
Chapter 6
Figure 6.1.
Network model (Benarous and Kadri 2018).
Figure 6.2.
Message sequence chart of the pseudonym/certificate refilling request.
Figure 6.3.
Message sequence chart of the anonymous authentication scheme.
Figure 6.4.
Vehicle’s and RA’s HLPSL specification code
Figure 6.5.
Result of the specified protocol using SPAN and AVISPA
Figure 6.6.
Message sequence chart of the specified authentication method.
Figure 6.7.
Message sequence chart of the authentication method in the presence of an intruder.
Chapter 7
Figure 7.1.
Vehicle Ad hoc Network illustration under attacker’s observations.
Figure 7.2.
Investigating the impact of varying ∆t and k-fake on the privacy, number of sent and received messages.
Figure 7.3.
Number of pseudonyms used per vehicle
Figure 7.4.
Ratio of tracked vehicles
Figure 7.5.
Ratio of linked pseudonyms
Figure 7.6.
Ratio of tracked vehicle.
Figure 7.7.
The proposed pseudonym changing scheme.
Figure 7.8.
Pseudonym changing process
Figure 7.9.
Number of pseudonyms used
Figure 7.10.
Ratio of tracked vehicles
Figure 7.11.
Ratio of linked pseudonyms
Figure 7.12.
Ratio of tracked vehicle for both solutions.
Chapter 8
Figure 8.1.
The number of IoT connected devices (based on the statistics of Heinze (2016))
Figure 8.2.
Network model.
Figure 8.3.
General description of the cloud-enabled Internet of vehicles.
Figure 8.4.
Diagram of the proposed identifier changing scheme
Figure 8.5.
Ratio of tracked vehicles
Figure 8.6.
Ratio of tracked vehicles for each attack.
Figure 8.7.
Ratio of tracked vehicles for both solutions.
Figure 8.8.
Syntactic attacker’s ratio of tracked vehicles for both solutions.
Figure 8.9.
Semantic attacker – ratio of linked identifiers.
Figure 8.10.
Observation mapping attack – ratio of the tracked vehicle.
Figure 8.11.
Ratio of tracked vehicles
Figure 8.12.
Ratio of tracked vehicles for each attack.
Figure 8.13.
Ratio of tracked vehicles.
Figure 8.14.
Syntactic attacker’s ratio of tracked vehicles.
Figure 8.15.
Semantic attacker (A. ratio of linked identifiers,
B. ratio of tracked vehicles).
Figure 8.16.
Observation mapping attack – ratio of tracked vehicles.
Figure 8.17.
Pseudonym and VMID change strategy
Figure 8.18.
Attacker’s tracking ratio for each attack.
Figure 8.19.
Average ratio of detected vehicles
Figure 8.20.
Average anonymity set, entropy and normalized entropy.
Figure 8.21.
Average ratio of detected vehicles.
Figure 8.22.
Ratio of tracked vehicles – semantic attack.
Figure 8.23.
Ratio of tracked vehicles – syntactic attack.
Figure 8.24.
Ratio of detected vehicles – observation mapping.
Chapter 9
Figure 9.1.
Blockchain architecture layers
Figure 9.2.
Transaction illustration
Figure 9.3.
Block structure
Figure 9.4.
Chains of blocks (blockchain).
Figure 9.5.
Blockchain-based pseudonym management for vehicular networks.
Figure 9.6.
Certifying transaction structure
Figure 9.7.
Revocation certificate structure
Figure 9.8.
Message authentication.
Figure 9.9.
Attack tree for the vehicular PKI and our proposed framework.
List of Tables
Chapter 2
Table 2.1.
Non-cooperative change strategies
Table 2.2.
Silence-based change strategies
Table 2.3.
Infrastructure-based mix-zone change strategies
Table 2.5.
Hybrid change strategies
Table 2.6.
Pseudonym change strategies – recap
Table 2.7.
Advantages and disadvantages of authentication types
Chapter 3
Table 3.1.
BAN logic notations
Table 3.2.
HLPSL simplified user guide (A. Team 2006a; 2006b)
Table 3.3.
Comparative study of simulation tools
Chapter 6
Table 6.1.
Illustration of brute-force estimated time of execution and number of combinations
Chapter 7
Table 7.1.
Simulation parameters of the attacker
Table 7.2.
Simulation parameters
Chapter 8
Table 8.1.
Simulation parameters for the vehicles
Table 8.2.
Comparative study between our proposed solution and the proposed solution by Kang et al. (2016)
Table 8.3.
Simulation parameters
Chapter 9
Table 9.1.
Standard grade chart
Table 9.2.
Probability of occurrence
Table 9.3.
Comparative study between our proposed framework and the vehicular PKI
Guide
Cover
Table of Contents
Title Page
Copyright
Begin Reading
Index
End User License Agreement
Pages
i
ii
iii
iv
xi
xii
xiii
xiv
xv
xvi
xvii
xviii
xix
xx
xxi
xxii
xxiii
xxiv
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
To my beloved parents,my cherished brothers,my dear sistersLeila Benarous
To Afaf, Nada, Nour, and RamiSalim Bitam
To my beloved young daughter IntissarAbdelhamid Mellouk
New Generation Networks Set
coordinated byAbdelhamid Mellouk
Volume 1
Security in Vehicular Networks
Focus on Location and Identity Privacy
Leila BenarousSalim BitamAbdelhamid Mellouk
First published 2022 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2022
The rights of Leila Benarous, Salim Bitam and Abdelhamid Mellouk to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2022941634
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-848-1
Preface
Vehicular networks are formed by connected vehicles. They were initially developed to ensure the safety of their users and extend Internet services to the road. They offer various types of services and applications to road users to make their trips more enjoyable and more comfortable. However, cyber activity can result in the creation of new types of risks, such as blackmailing, data trading or profiling. Worse still, they may have an impact on the safety of on-board users and may lead to road accidents. The risks arise from tracking and privacy violations through the interception of messages, the exchange of which is necessary for participation in the network. Privacy and security are the major issues that need to be resolved in order for vehicular networks to be implemented in real-world applications.
This book proposes privacy-preserving solutions that protect the user’s identity and on-road location to prevent tracking from occurring. Our solutions have been tested experimentally and analytically to evaluate their performance against a strong attacker model. Initially based on the work conducted by Leila Benarous as part of her PhD thesis, this book has developed into a resource to facilitate the understanding of vehicular networks and the technologies they employ, as well as their various types. It highlights the significance of their associated privacy and security issues and their direct impact on the safety of users. Furthermore, it includes two anonymous authentication methods that preserve identity privacy, as well as five schemes that preserve location privacy in vehicular ad hoc networks (VANETs) and cloud enabled Internet of vehicles (CE-IoV) respectively. The design of a new privacy-aware blockchain-based pseudonym management framework is also included. The framework is secure, distributed and public, ensuring revocation, non-repudiation, authenticity and integrity, which are the fundamental security requirements. The proposal was developed as a potential replacement for the vehicular public key infrastructure (VPKI).
To give a complete historical account of the research that led to the present form of the subject would have been impossible. It is thus inevitable that some topics have been covered in less detail than others. The choices partly reflect personal taste and expertise, and also a preference for the very promising research and recent developments in the field of technology-based security in vehicular networks.
This book is a start, but many questions remain unanswered. We hope that it will inspire a new generation of investigators and investigations. This book would not have been possible without the hard work of the following people: Professor Bouridane Ahmed, Professor Guizani Mohsen, Professor Kadri Benamar, Professor Belabbaci Youcef, Professor Ouinten Youcef, Dr. Saadi Boudjit, Dr. Bensaad Mohammed Lahcen and Dr. Oubbati Omar Sami. We give particular thanks to Mr. Djoudi Mohamed as well as the late Dr. Yamani Ahmed for their endless support. Finally, we thank everyone who has contributed to the success of this work. The authors hope that you will enjoy reading this book and get many helpful ideas and overviews for your own study.
June 2022Leila BENAROUSSalim BITAMAbdelhamid MELLOUK
List of Acronyms
AASS
Average Anonymity Set Size
ASS
Anonymity Set Size
AVISPA
Automated Validation of Internet Security Protocols and Applications
BAN
Burrows, Abadi and Needham
BB
Big Brother
BC
Blockchain
BCN
Beacon
CA
Certifying Authority
CaaS
Cooperation-as-a-Service
CE-IoV
Cloud Enabled Internet of Vehicles
CIoV
Cognitive IoV
CL-atSe
Constraint Logic-based Attack Searcher
CLPPS
Cooperative-based Location Privacy-Preserving Scheme for Internet of Vehicles
CM
Cloud Manager
COO
Cooperation
CPS
Certified Pseudonyms
CPU
Central Processing Unit
CRL
Certificate Revocation List
CS
Content Store
CSLPPS
Concerted Silence-based Location Privacy-Preserving Scheme for Internet of Vehicles
CVD
Change VMID
CVDNN
Cloud-enabled Vehicle Data Named Networks
DataPk
Data Packet
DC
Do the Change
DDoS
Distributed Denial of Service
DoS
Denial of Service
DRL
Data Reliability
DSRC
Dedicated Short-Range Communications
EDGE
Enhanced Data rates for GSM Evolution
ERGS
Electronic Route Guidance System
FCC
Federal Communications Commission
FIB
Forwarding Information Base
GPA
Global Passive Attacker
GPRS
General Packet Radio Service
GPS
Global Positioning System
GSM
Global System for Mobile
HLPSL
High-Level Protocol Specification Language
HMI
Human–Machine Interface
HSDPA
High-Speed Downlink Packet Access
HVC
Hybrid Vehicular Cloud
IC-NoW
Information Centric Network on Wheels
InaaS
Information-as-a-Service
IntPk
Interest Packet
IoT
Internet of Things
IoV
Internet of Vehicles
ITS
Intelligent Transport System
LBS
Location-Based Service
LTCA
Long-Term Certification Authority
MANET
Mobile Ad hoc Network
MITM
Man in the Middle
MSC
Message Sequence Chart
NaaS
Network-as-a-Service
NDN
Named Data Networks
NoW
Network on Wheels
NRV
New Reputation Value
NS
Network Simulator
OBU
On-Board Unit
OFMC
On-the-Fly Model Checker
ORV
Old Reputation Value
PCA
Pseudonym Certification Authority
PIT
Pending Interest Table
PKI
Public Key Infrastructure
PoS
Proof of Stake
PoW
Proof of Work
PRA
Pseudonym Resolution Authority
PROMETHEUS
Program for European Traffic with Highest Efficiency and Unprecedented Safety
PSD
Pseudonym
PUF
Physically Unclonable Functions
QoS
Quality of Service
RA
Regional Authority
RCA
Root Certifying Authority
RCP
Resource Command Processor
RDC
Ready to Do the Change
RFID
Radio Frequency Identification
RS
Registration Server
RSU
Road Side Units
SATMC
Satisfiability-based Model Checker
SC
Service Continuity
SDN
Software-Defined Networks
SDVN
Software-Defined Vehicular Networks
SF
Selfishness
SIG
Signature
SPAN
Security Protocol Animator
StaaS
Storage-as-a-Service
TA
Trusted Authority
TA4SP
Tree Automata for Security Protocols
TMN
Testimony
TPD
Tamper Proof Device
Tx
Transaction
UMTS
Universal Mobile Telecommunication System
V2H
Vehicle-to-Human
V2I
Vehicle-to-Infrastructure
V2N
Vehicle-to-Network
V2S
Vehicle-to-Sensors
V2V
Vehicle-to-Vehicle
V2X
Vehicle-to-Everything
VANET
Vehicular Ad hoc Network
VC
Vehicular Cloud
VCS
Vehicular Cloud Services
VIN
Vehicle Identification Number
VM
Virtual Machine
VMID
Virtual Machine Identifier
VN
Vehicular Networks
VPKI
Vehicular Public Key Infrastructure
VS
Verification Server
VuC
Vehicle using the Cloud
Introduction
I.1. Introduction
Vehicles are continuously evolving, from a simple means of transport to a powerful computer on wheels. The overwhelming concerns about reducing road causalities and protecting the environment are pushing researchers and the industry to develop smart vehicles that are safer and more eco-friendly. Extending the Internet and networking concepts to the road is becoming a necessity rather than a luxury.
Vehicular networks (VNs) are formed by vehicles, road infrastructures, on-road devices and sensors. They were originally created to safeguard on-roads users and reduce the number of accidents and casualties. They are now developed to provide high-quality infotainment services. The broadcast of real-time road information such as construction work, traffic, accidents, weather and road conditions helps road users to easily and safely plan their trips. VNs offer various applications, including autonomous driving. In fact, autonomous driving can be achieved by smart vehicles independently, and can also be realized via VNs, where road data is exchanged over the network to make automatic adaptive driving decisions. The vision of researchers and the industry is not limited to offering on-road safety-related applications, but also aims to extend the Internet infotainment to road-edge. As VNs manage vehicular traffic in a smooth way, they result in reducing fuel consumption and emitted toxins and gases. Therefore, they help in protecting the environment.
However, although VNs are developed to save users’ lives and offer them various on-road services, and despite the benefits they bring in protecting the environment, they result in breaching the privacy of road users. This is due to their nature that requires the broadcast of real-time spatio-temporal identifying data. This identifying data can be used to perform profiling and tracking attacks on users. Therefore, security and privacy are two fundamental issues that must be preserved and ensured to safely deploy these networks.
I.2. Motivation
The damage caused by cyber-system security breaches is significant in terms of moral and financial implications, as well as the impact on human life. The technology news reports devastating security violation launched against top high-tech corporations yearly. VNs extend computers and the cyber world to roads. Therefore, fatalities resulting from security and privacy violation on-road are even more tremendous because they are directly related to the user safety.
A vehicle should not be tracked via its on-road cyber activity. Its user’s identity should not be known nor extracted from the vehicle’s exchanged messages. If a vehicle is successfully tracked from its cyber activity by an attacker, they may learn its driver’s routines, parsed trajectories, hideouts and frequented places. The attacker may track (stalk) the vehicle to trade its user’s data for profit, out of personal interest or to blackmail the vehicle owner using collected secrets. The consequences of leaking trajectory data about the user may give rise to serious risks, such as planning traffic congestion or accidents along frequented routes. Even worse, a malicious attacker may even execute on-road assassination. To avoid these serious consequences and ensure the safe use of VNs, we concentrated our research on developing security and privacy solutions. These solutions reduce the tracking risks for VN users.
I.3. Objectives
Our research aims to preserve the privacy and security of VN users. More precisely, our interest lies in protecting identity and location privacy as they are interconnected. Exposure of one results in the violation of the other, leading to the aforementioned fatalities. The cause of privacy vulnerability in VNs is the broadcast of periodic state messages needed for safety applications, which are sent wirelessly in clear with high frequency. Moreover, they contain accurate, real-time identity and spatio-temporal information. Their easy interception results in vehicle trajectory tracking. Furthermore, VNs also demand the assurance of non-repudiation (accountability), authentication and revocation of mischievous nodes to maintain their reliability. In fact, these requirements go against privacy demands. Therefore, when developing a solution, both privacy and security requirements should be guaranteed in a balanced way. The existing solutions to protect location privacy use temporal identities known as pseudonyms. These pseudonyms are frequently updated through change strategies aiming to reduce their inter-linkability. Unlinkability between updated pseudonyms also protects location (trajectory) privacy. Moreover, the use of pseudonyms ensures anonymity. Therefore, the majority of existing solutions are designed to protect anonymity and limit linkability to prevent tracking.
Identity privacy may be further exposed if repeatedly used to authenticate the vehicle to infrastructures, authorities and service providers. Consequently, we concentrate on developing privacy-preserving authentication schemes, also known as anonymous authentication methods. While designing these solutions, we intended to make them resilient to security attacks that target VNs, such as Sybil attacks, and authentication systems.
Currently, VNs are authority-based, i.e. vehicle registration and the issuance of certificates are done by the authority. This authority ensures the correct functionality of the network through the revocation of misbehaving nodes and the tracing of honest nodes. This means that privacy is conditional in VNs; it is preserved from other vehicles and exposed to the authority when the vehicle misbehaves and disrupts the functionality of the network. Moreover, the authority provides the vehicle with security parameters, keys, certificates and algorithms. The authority-based system is known as the vehicular public key infrastructure (VPKI). The VPKI is preferred over the self-generated key system because it satisfies the main requirements needed in VNs, such as preventing Sybil attacks, guaranteeing conditional privacy, ensuring non-repudiation and revocation, etc. Therefore, most of the existing solutions for safeguarding privacy in VNs are built over the VPKI.
In the following, we explain the aims of this book:
– Our first objective is to understand VN characteristics and types, alongside a review of their security issues and sources. Our focus is on authentication and privacy issues.
– Our second objective is to ensure authentication without any violation of identity privacy. Nevertheless, privacy-preserving authentication methods, also known as anonymous authentication methods, may instigate other security infringements. Being anonymous may enable untraceable network exploitation. It may also disrupt network functioning. Furthermore, it contradicts non-repudiation and revocation requirements. Consequently, when developing anonymous authentication methods, we first thought of how to resolve the issues mentioned above.
– Our third objective relates to the development of infrastructure, crowd and road-map independent location privacy-preserving schemes for vehicular ad hoc networks. The solutions discussed are pseudonym update strategies, which maintain correct network functionality while reducing linkability. The solutions are designed to protect location privacy, even when used on low density roads where tracking is likely to occur.
– Our fourth objective is to design location privacy-preserving schemes for Internet of vehicles (IoV) road users. Our target is to reduce the linkability achieved from matching IoV location-based service queries with periodic beacon safety applications. Reducing linkability in turn reduces tracking. Developed solutions must not negatively interfere with network functionality nor cause service interruption.
– Our final objective is to propose a potential replacement for the central-based VPKI. The VPKI is secure and most of the existing solutions discuss its robustness from the researcher’s perspective. However, certificate issuance is most likely to be a paid service. Furthermore, the fact that it is centralized makes it prone to a single point of failure and the target of attacks. Lastly, VPKI deployment costs to cover and satisfy all the needs of the network vehicles’ pseudonyms are extremely high. We therefore design a distributed, cost-free blockchain-based pseudonym management framework as a potential replacement for VPKI. This framework ensures the security requirements of authenticity, privacy, non-repudiation, integrity and revocation. It relies on the network nodes (vehicles and infrastructures) to self-generate the pseudonyms and add them to the blockchain. The aim is to decrease the cost of the VPKI, provide a secure, distributed pseudonym management framework and prevent the single point of failure problem.
I.4. Book structure
This book is organized into nine chapters. The first four chapters are dedicated to a literature review. The remaining five chapters are based on some of our past contributions. A brief outline of each chapter is given below.
Chapter 1 aims to clarify the basic concepts related to VNs: their evolution, technology, architecture, characteristics and challenges. It also lists their standards, applications and real-world implementations. This chapter also includes public opinions about these networks. Most importantly, it enumerates the various types of VNs and highlights the key differences between them.
Chapter 2 introduces the reader to the privacy and security issues in VNs; particular attention is paid to identity and location privacy, as well as to authentication as a security issue. This chapter explains the privacy issue and sheds light on its importance and the potential consequences of its violation. It also answers the questions about why privacy is threatened, when, by whom and how. Similarly, the authentication issue is explained and its contradiction with privacy requirements is highlighted. This chapter also surveys prominent existing solutions for each issue separately.
Chapter 3 explains the security and privacy evaluation methodology, metrics and tools. We mention the key methods used in the literature and then explain our chosen methodology and our reasons for using it.
Chapter 4 studies security issues against an attacker model that deploys various types of attack, in order to evaluate performance in terms of its resilience to those attacks. In this chapter, we explain the security objectives and properties that should be maintained in VNs. We also mention key security challenges in these networks that are subject to research. Finally, we explain our attacker model for both authentication and privacy issues. We clarify its aims, types, means and attacks.
Chapter 5 defines the model for cloud-enabled vehicle data named networks where vehicles may share their resources with one another on the road. Resource sharing happens upon successful authentication and is done while ensuring privacy. To preserve privacy between the resource requester and the service provider, an anonymous reputation-based authentication is performed. The proposed mutual authentication method is proved to achieve its underlying aims using the BAN logic.
Chapter 6 includes privacy-preserving anonymous authentication that ensures privacy as well as security. The solution is used as the initial phase to request on-road pseudonym/certificate refilling. This process is repetitive and may lead to tracking if the identity is used repeatedly in the request, even if the communication is secure. The proposed authentication method ensures authenticity, integrity, non-repudiation and revocation. Furthermore, it is resilient to man-in-the-middle attacks, replay attacks, impersonation, brute force and Sybil attacks. We use the BAN logic to prove its correctness and SPAN and AVISPA to prove that it is safe, ensures the authentication aims and is resilient to well-known attacks.
Chapter 7 deals with location privacy in VNs, which is a critical issue. Trajectory tracking is risky, and results from accurate linkability between updated pseudonyms. The consequences of tracking may vary from stalking and blackmailing to assassination. Various solutions exist in the literature, aiming to reduce the linkability and tracking ratio. In this chapter, we propose two solutions that are road-, crowd- and infrastructure-independent. Both aim to reduce the linkability ratio, even when the vehicle is within low density roads. The solutions were analyzed by simulation against the attacker model defined in Chapter 4. The first proposal reduced the tracking ratio to an average of 27%. The second proposal was even better, with an average tracking ratio of 10.4%.
Chapter 8 presents the Internet of vehicles, which is the evolution of VANET. It relies on cloud computing to provide a wider range of more stable and global services. Consecutively, the risks to privacy come from linking the location-based services used by the vehicle through the IoV. Also, its safety-related application participation is necessary to maintain the correct functionality of networks. In this chapter, we propose three location privacy-preserving solutions that take the above risks into account. The solutions are tested through simulation against the attacker model defined in Chapter 4. Each solution is the amelioration of its predecessor. These ameliorations aim to reduce the tracking ratio: the lower this ratio, the higher the level of privacy provided by the solution. The ratios obtained are 30%, 16% and 10% on average for the three proposals respectively.
Chapter 9 proposes a potential replacement framework to the vehicular PKI that suffers from a single point of failure and is costly to deploy. The framework is based on the blockchain. It preserves privacy even though it is public. It ensures authentication, revocation, non-repudiation and integrity. It inherits the security strength of blockchains, prevents alterations and ensures availability. The framework is a blockchain of two public blockchains. The first blockchain is permissionless, and contains vehicle-generated pseudonyms. The second blockchain is permissioned, and contains revoked pseudonyms. Our framework provides the same requirements ensured by the VPKI, while ensuring a higher level of security.
It is worth noting here that some of the work presented in these chapters has already been published in several journals and featured at numerous conferences.
1Vehicular Networks
1.1. Introduction
Vehicular networks are at the core of the intelligent transport system (ITS). Interest in these networks is constantly growing due to the need to reduce road fatalities, which result in immense yearly losses in terms of human lives, physical and mental health repercussions, property damage and financial losses. They were initially developed to ensure the safety of road users by providing them with accurate prior knowledge about the traffic, road conditions and shortcuts. They were also intended to provide users with safe, comfortable trips in their autonomous vehicles. Moreover, vehicle networks help to lower fuel consumption by reducing traffic jams and streamlining the driving experience.
In this type of network, the vehicles are the main nodes. They are also referred to as computers on wheels in the literature. The vehicles are equipped with various types of sensors for external and internal roles such as sensing proximity or engine heat. They also contain a global positioning system (GPS) to localize the vehicle, cameras, radar and lidar to sense the surroundings and detect obstacles and road conditions. The on-board unit (OBU) is the brain of the vehicle and computer controlling it; it ensures the vehicle’s correct functioning and processes the sensed data. The OBU gives the vehicle the trait of smartness, which is the same reason they are known as “smart vehicles”. They also have network interfaces that are used to communicate, alongside a storage space to save sensed data, security programs and received messages.
The vehicular networks encompass several types of networks in which the main type of node is a vehicle. It comprises the autonomous vehicles, Vehicular Ad hoc Networks (VANETs), vehicular data named networks, vehicular cloud computing, Internet of vehicles, etc. The appearance of these types is due to the evolution of the on-road users’ service demands and corresponding answering applications. The earliest applications of VNs were safety-related, oriented towards assisting the driver. Then, the infotainment applications became a necessity later on. Lastly, the Internet and cloud computing were extended to road-edge in order to provide road users with their services.
This chapter reviews vehicular networks, their evolution and their applications. It depicts the yearly fatalities caused by vehicle causalities and the assessments of the benefits of using the vehicular networks to ensure safety, alongside the evaluation of their market value. It describes the vehicular networks’ evolution as part of the intelligent transportation systems and road automation projects. It also explains the components of intelligent vehicles and the architectures of vehicular networks. It describes the main distinguishable characteristics of the vehicular networks. It enumerates the issues and technical challenges halting the vehicular networks’ real implementations. Furthermore, it lists the wireless technologies that could potentially be used in vehicular networks. It outlines the vehicular networks regulating standards and explains the vehicular network’s different existing types along with its test beds. Lastly, it reviews the public opinion and acceptance of the technology.
1.2. Motivation by numbers
The following statistics emphasize the importance of vehicular networks (Contreras-Castillo et al. 2018):
– approximately 1.3 million people die every year;
– more than 7 million people are injured;
– nearly 8 million traffic accidents are recorded;
– estimated wasted time because of traffic jams and accidents is over 90 billion hours;
– vehicles produce 220 million tons of carbon;
– the expected global market of the Internet of Everything may reach 14.4 trillion dollars by 2022 (Bonomi 2013) and the value of the Internet of vehicles (IoV) alone was estimated to be 115.26 billion Euros by 2020 (Contreras-Castillo et al. 2018);
– more importantly, autonomous vehicles usage would eliminate 80–90% of vehicles’ accidents and crashes (Maglaras et al. 2016);
– a rough estimation (Bai and Krishnamachari 2010) states that 100% market penetration by vehicular networks would take 14–15 years from its initial deployment date.
With these statistics, it is abundantly clear that vehicular networks are most likely serving the purposes they were developed for in reducing car accidents, injuries, mortalities, pollution, etc. Academics and industry are doing their best to concretize and market this technology. What is left is to convince the public and draw attention to its benefits.
1.3. Evolution
Automated roads and the creation of self-driving vehicles have been the dream of various researchers and industry sectors. General Motors was the leader in exhibiting the basic concepts of road automation, known as “Futurama”, at the 1939 World Fair. In 1970, a follow-up proposition came from the United States, which is the Electronic Route Guidance System (ERGS). It pilots the drivers to their destination by decoding and transmitting routing instructions to and from roadsides at intersections. In Japan, between 1973 and 1979, the Comprehensive Automobile Traffic Control System was launched. The project aimed to reduce air pollution and traffic congestion and prevent accidents. It also aimed to provide the driver with appropriate route directions thanks to the accurate information and warnings received (Gerla et al. 2014). In Europe, the PROMETHEUS (Program for European Traffic with Highest Efficiency and Unprecedented Safety) framework was introduced in 1986 and launched in 1988.
The term vehicular ad hoc networks was first coined by Ken Laberteaux in the first International Workshop on Vehicular Ad hoc Networks (VANET) held in Philadelphia in 2004 (Hartenstein and Laberteaux 2010). VANET is considered to be the first commercialized version of Mobile Ad-hoc Networks and one of its most promising applications, aiming to automate the roads and ensure user comfort and safety. It draws the community nearer to realizing the self-driving vehicles’ vision (Gerla et al. 2014).
Ever since the VANET became a hot topic, various related consortiums and projects were launched yearly. We mention a few of them as an example: FleeNET, CarTalk2000, Car2Car consortium, PReVENT, Network on Wheel (NoW), MobiVip, etc. (Meraihi et al. 2008).
Starting from 2010, a new vehicular networks type appeared, the vehicular cloud (VC) concept, which combines cloud computing with vehicular networks. The VC takes advantage of the vehicles’ sensing, calculation and storage capacities to extend the clouds that offer various kinds of stable services (Gu et al. 2013). As of 2014, researchers focused on the Internet of vehicles (Gerla et al. 2014; Yang et al. 2014), which is the evolution of VANETs and an instantiation of the Internet of Things.
In 2009, Google’s first autonomous vehicle project started. It continued its tests and trials until 2015, when it first hit the public road. In 2016, the project became independent and was named Waymo under Alphabet, a self-driving technology company. Meanwhile, major car companies such as Renault, Mercedes, Tesla and Audi have been competing to launch their own self-driving vehicles (top companies for self-driving vehicles).
1.4. Architecture
Autonomous vehicles, also known as self-driving vehicles, are smart. They have two types, illustrated in Figure 1.1. The first type is the self-dependent (self-contained) vehicle, relying only on its smarts and computational capacities to process sensed data, execute instructions and make decisions. The second type is the interdependent vehicle, where a vehicle either exchanges data and instructions with a control server via Vehicle-to-Infrastructure (V2I), or, it is connected to other network nodes (vehicles) via Vehicle-to-Everything (V2X) to interchange sensed data. The second type is denoted as vehicular networks. Although the types differ, the design and components of the smart vehicles are similar (Glancy 2012).
Figure 1.1.Autonomous vehicles
Vehicular networks in general are built upon the VANETs basic architecture. They are composed of vehicles as mobile nodes known as on-board units (OBU) and road side units (RSU) as static infrastructures. The OBU stores, locates, calculates and sends messages over a network interface (Gerla et al. 2014). It is composed of a read/write memory used to store/fetch information, a Resource Command Processor (RCP), a user interface and a network device using IEEE 802.11p radio technology for short-range wireless communication (Al-Sultan et al. 2014).
The RSUs broadcast advertisement and road information; they also spread data sent by OBUs (Gerla et al. 2014). They are equipped with network interfaces using IEEE 802.11p radio technology for a dedicated short-range communication, and wired interfaces for communication with the other infrastructures in the network (Al-Sultan et al. 2014). The basic components of a smart vehicle were initially summed up into the use of GPS, radar, sensors, an on-board computer (for processing and storage), network interfaces and a human-friendly interface.
However, the technology is constantly developing, with numerous carmakers competing on intelligent vehicles’ implementations and testing. Therefore, the components are more sophisticated, refined and advanced. For illustration purposes, we study the components of a Google smart car as an example of the current design and technology in use.
A Google vehicle uses a distinguishable set of hardware and software technologies, and we mention a few of them (national):
– laser range finder (lidar, 360° camera), which can create 3D images of objects within a 200 m range and calculate the distances;
– front camera for near vision, which can detect front objects, pedestrians, obstacles, traffic lights, road signs, etc.;
– bumper-mounted radar, which are mounted on the front and rear of the vehicle to avoid bumps and crashes with vehicles, pedestrians and obstacles;
– an aerial on the rear of the vehicle that reads precise geo-location; this is done by matching the GPS location received from the satellite with the sensed location and internal map to render the location more accurately;
– ultrasonic sensors on rear wheels, which are used to keep track of the wheel’s movement and detect the obstacle on the rear of the vehicles;
– the altimeters, gyroscopes and tachymeters inside the vehicle give precise information about its position;
– synergistic combining of sensors. The vehicle has a set of sensors for various purposes, all of the sensed data is combined and processed by the on-board unit to help achieve safe self-driving;
– in-built programmed maps, human behavior, data processing and decisional algorithms.
1.5. Characteristics
Vehicular networks have a particular nature and characteristics that differentiate them from other networks:
– their topology is highly dynamic due to the vehicles’ high speed;
– predictable patterned movements defined by the roads and paths the vehicles run through;
– frequently disconnected networks causing a delay and potential loss of messages;
– the use of different types of external and internal sensors for various purposes;
– unlimited battery power and large storage capacity (Kumar et al. 2013);
– variable network density in roads (in rural areas, in the city, during daytime, during nighttime, etc.) (Al-Sultan et al. 2014);
– the various obstacles on road and surrounding it, such as trees and tall buildings;
