Abstract
Internet of Vehicles (IoVs) have become a focused research area in recent years due to the rapid digitalization of vehicles and transportation systems. While being able to enhance traffic efficiency and transport safety, IoVs also bring up novel cybersecurity issues. Although cryptography-based methods can solve the problem of identity trust, they can not ensure trustworthy behaviors within the network. In order to undermine the performance of IoVs, authenticated attackers can conduct a series of attacks such as tampering with received messages and black hole attacks. Given the problems above, trust management mechanisms are employed in IoVs to evaluate the trustworthiness of network participants. This chapter first gives a brief introduction to IoVs and the security challenges. Then this chapter analyses the security requirements and common attacks under IoVs. Moreover, existing trust models are reviewed and compared. Finally, this chapter discusses simulation tools for trust management in IoVs and identifies future research opportunities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Abbreviations
- IoV:
-
Internet of Vehicle
- ITS:
-
Intelligent Transportation System
- RSU:
-
Road Side Unit
- PKI:
-
Public Key Infrastructure
- BSM:
-
Basic Safety Message
- TA:
-
Trusted Authority
- DDoS:
-
Distributed Denial of Service
- DTMS:
-
Distributed Trust Management System
References
China Society of Automotive Engineers, in Cooperative Intelligent Transportation System-Vehicular Communication Application Layer Specification and Data Exchange Standard (Phase I) CSAE 53-2020 (Beijing, 2020)
J.-H. Cho, K.S. Chan, S. Adali, A survey on trust modeling. ACM Comput. Surv. (CSUR) 48, 1–40 (2015)
Z. Lu, G. Qu, Z. Liu, A survey on recent advances in vehicular network security, trust, and privacy. IEEE Trans. Intell. Transp. Syst. 20, 760–776 (2019). https://doi.org/10.1109/TITS.2018.2818888
U. Khan, S. Agrawal, S. Silakari, Detection of malicious nodes (DMN) in vehicular ad-hoc networks. Procedia Comput. Sci. 46, 965–972 (2015). https://doi.org/10.1016/j.procs.2015.01.006
U.F. Minhas, J. Zhang, T. Tran, R. Cohen, A multifaceted approach to modeling agent trust for effective communication in the application of mobile ad hoc vehicular networks. IEEE Trans. Syst., Man, Cybern. Part C (Appl. Rev.) 41, 407–420 (2011). https://doi.org/10.1109/TSMCC.2010.2084571
C. Ge, L. Zhou, G.P. Hancke, C. Su, A Provenance-aware distributed trust model for resilient unmanned aerial vehicle networks. IEEE Internet Things J. 8, 12481–12489 (2021). https://doi.org/10.1109/JIOT.2020.3014947
Q. Wu, Q. Liu, L. Zhang, Z. Zhang, A trusted routing protocol based on GeoDTN+Nav in VANET. China Commun. 11, 166–174 (2014). https://doi.org/10.1109/CC.2014.7085617
P. Cheng, K.C. Lee, M. Gerla, J. Härri, GeoDTN+Nav: Geographic DTN routing with navigator prediction for urban vehicular environments. Mob. Netw. Appl. 15, 61–82 (2010). https://doi.org/10.1007/s11036-009-0181-6
L. Zhang, X. Zhang, C. An, C. Tang, A reputation-based incentive scheme for delay tolerant networks. Acta Electron. Sin. 42, 1738–1743 (2014). https://doi.org/10.3969/j.issn.0372-2112.2014.09.012
M. Raya, P. Papadimitratos, V.D. Gligor, J.-P. Hubaux, On data-centric trust establishment in ephemeral ad hoc networks, in IEEE INFOCOM 2008—The 27th Conference on Computer Communications (2008), pp. 1238–1246
Z. Huang, S. Ruj, M.A. Cavenaghi, M. Stojmenovic, A. Nayak, A social network approach to trust management in VANETs. Peer-To-Peer Netw. Appl. 7, 229–242 (2014). https://doi.org/10.1007/s12083-012-0136-8
A. Wu, J. Ma, S. Zhang, RATE: a RSU-aided scheme for data-centric trust establishment in VANETs. In: 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (IEEE, Wuhan, China, 2011), pp. 1–6
X. Zhang, R. Li, W. Hou, J. Shi, Research on Manhattan distance based trust management in vehicular ad hoc network. Secur. Commun. Netw. 2021, 1–13 (2021). https://doi.org/10.1155/2021/9967829
A. Le, C. Maple, Shadows don’t lie: n-sequence trajectory inspection for misbehaviour detection and classification in VANETs. In: 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) (2019), pp. 1–6
K. Zaidi, M.B. Milojevic, V. Rakocevic, A. Nallanathan, M. Rajarajan, Host-based intrusion detection for VANETs: a statistical approach to rogue node detection. IEEE Trans. Veh. Technol. 65, 6703–6714 (2016). https://doi.org/10.1109/TVT.2015.2480244
D. Rawat, G. Yan, B.B. Bista, M. Weigle, Trust on the Security of Wireless Vehicular Ad-Hoc Networking (Ad Hoc & Sensor Wireless Networks, 2015)
R. Shrestha, S.Y. Nam, Trustworthy event-information dissemination in vehicular ad hoc networks. Mob. Inf. Syst. 2017, 1–16 (2017). https://doi.org/10.1155/2017/9050787
W. Li, H. Song, ART: an attack-resistant trust management scheme for securing vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 17, 960–969 (2016). https://doi.org/10.1109/TITS.2015.2494017
C. Sommer, R. German, F. Dressler, Bidirectionally Coupled network and road traffic simulation for improved IVC analysis. IEEE Trans. Mob. Comput. (TMC) 10, 3–15 (2011). https://doi.org/10.1109/TMC.2010.133
B. Hilt, M. Berbineau, A. Vinel, A. Pirovano, simulation of convergent networks for intelligent transport systems with VSimRTI, in Networking Simulation for Intelligent Transportation Systems: High Mobile Wireless Nodes (2017), pp. 1–28
A. Keränen, J. Ott, T. Kärkkäinen, The ONE simulator for DTN protocol evaluation, in Proceedings of the 2nd International Conference on Simulation Tools and Techniques. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (BEL, Brussels, 2009), pp. 1–10
F. Li, J. Wu, LocalCom: a community-based epidemic forwarding scheme in disruption-tolerant networks, in 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (2009), pp. 1–9
S. Yang, J. Li, Z. Liu, S. Wang, Managing trust for intelligence vehicles: a cluster consensus approach, in Proceedings of the Second International Conference on Internet of Vehicles—Safe and Intelligent Mobility, vol. 9502 (Springer-Verlag, Berlin, Heidelberg, 2015), pp. 210–220
R. Hussain, W. Nawaz, J. Lee, J. Son, J.T. Seo, A Hybrid Trust Management Framework for Vehicular Social Networks, in H.T. Nguyen, V. Snasel (eds) Computational Social Networks (Springer International Publishing, Cham, 2016), pp. 214–225
C.A. Kerrache, N. Lagraa, R. Hussain, S.H. Ahmed, A. Benslimane, C.T. Calafate, J.-C. Cano, A.M. Vegni, TACASHI: trust-aware communication architecture for social internet of vehicles. IEEE Internet Things J. 6, 5870–5877 (2019). https://doi.org/10.1109/JIOT.2018.2880332
B. Jedari, F. Xia, H. Chen, S.K. Das, A. Tolba, Z. AL-Makhadmeh, A social-based watchdog system to detect selfish nodes in opportunistic mobile networks. Futur. Gener. Comput. Syst. 92:777–788 (2019).https://doi.org/10.1016/j.future.2017.10.049
L. Codeca, R. Frank, S. Faye, T. Engel, Luxembourg SUMO traffic (LuST) scenario: traffic demand evaluation. IEEE Intell. Transp. Syst. Mag. 9, 52–63 (2017). https://doi.org/10.1109/MITS.2017.2666585
R.W. van der Heijden, T. Lukaseder, F. Kargl, VeReMi: A dataset for comparable evaluation of misbehavior detection in VANETs, in R. Beyah, B. Chang, Y. Li, S. Zhu (eds) Security and Privacy in Communication Networks. SecureComm 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 254 (Springer, Cham, 2018). https://doi.org/10.1007/978-3-030-01701-9_18
P.A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, E. Wießner, Microscopic traffic simulation using SUMO, in The 21st IEEE International Conference on Intelligent Transportation Systems (IEEE, 2018)
T.R. Henderson, M. Lacage, G.F. Riley, C. Dowell, J. Kopena, Network simulations with the ns-3 simulator. SIGCOMM demonstration 14, 527 (2008)
A. Varga, OMNeT++, in Modeling and Tools for Network Simulation (Springer, 2010), pp. 35–59
J. Kamel, M.R. Ansari, J. Petit, A. Kaiser, I.B. Jemaa, P. Urien, Simulation framework for misbehavior detection in vehicular networks. IEEE Trans. Veh. Technol. 69, 6631–6643 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Zhang, Y., Lv, C., Cheong, C., Cao, Y. (2023). An Introduction to Trust Management in Internet of Vehicles. In: Zhu, Y., Cao, Y., Hua, W., Xu, L. (eds) Communication, Computation and Perception Technologies for Internet of Vehicles. Springer, Singapore. https://doi.org/10.1007/978-981-99-5439-1_13
Download citation
DOI: https://doi.org/10.1007/978-981-99-5439-1_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5438-4
Online ISBN: 978-981-99-5439-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)