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Trust Management for Software-Defined Heterogeneous Vehicular Ad Hoc Networks

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Security, Privacy and Trust in the IoT Environment

Abstract

Over the past few decades, a considerable amount of interest has been observed (in both academia and industry) for improving the overall road transportation across the globe, primarily to enhance the safety of vehicular passengers and vulnerable road pedestrians. Vehicular Ad hoc Networks (VANETs) are anticipated to play a critical role in smart cities and Internet of Things (IoT) domain. Instead, it is expected that a new paradigm of Internet of Vehicles (IoV) will soon become an important component of the IoT. Also, since the connected vehicles primarily disseminate safety-critical information, it is imperative to have an extremely secure and trusted network so that critical data information (or any other sort of sensory data information) could be traversed with extreme reliability and authenticity. Unlike conventional wired networks, vehicular networks are highly dynamic, distributed and of open nature, and are, therefore, susceptible to various attacks such as replay, spoofing, eavesdropping, man-in-the-middle, distributed denial-of-service, blackhole, grayhole, Sybil and other malware attacks. To address the same, numerous mechanisms have been proposed in the literature, mainly relying on traditional cryptography techniques. Nevertheless, cryptography-based solutions are not effective in VANETs since nodes in this network are highly dynamic and distributed across the network. Moreover, the network infrastructure cannot be guaranteed permanently, and cryptographic solutions may also get compromised due to insider attacks in a network. This chapter aims to provide an in-depth investigation of a diverse range of security attacks challenging the actual realisation of vehicular networks. In contrast to conventional security (cryptography-based) solutions, it brings forth the need for trust management for securing vehicular networks (a concept still in its early stages of development) for ensuring reliability, authenticity and relevance by revoking both malicious and selfish nodes. It also briefly highlights the need for trust models and illustrates the characteristics of data-oriented trust models, entity-oriented trust models and hybrid trust models. Furthermore, since the conventional networks are being transformed via the promising yet emerging notion of software-defined networking (SDN), a brief discussion is presented so as to illustrate how a reconfigurable, reprogrammable and agile infrastructure can help in guaranteeing more secure vehicular networking platforms which are indispensable for futuristic Intelligent Transportation System (ITS) applications and services.

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Notes

  1. 1.

    Vehicle platooning considerably reduces the inter-vehicular distances in contrast to the ones recommended for conventional (manual) driving. This helps in achieving shared objectives instead of uncoordinated individual decisions, increasing the road capacity as a large number of cars can be packed in finite road space and an increase in energy efficacy due to a reduction in aerodynamic drag [49].

References

  1. Sun SH, Hu JL, Peng Y, Pan XM, Zhao L, Fang JY (2016) Support for vehicle-to-everything services based on LTE. IEEE Wirel Commun 23(3):4–8

    Article  Google Scholar 

  2. Seo H, Lee KD, Yasukawa S, Peng Y, Sartori P (2016) LTE evolution for vehicle-to-everything services. IEEE Commun Mag 54(6):22–28

    Article  Google Scholar 

  3. Choi J, Prelcic NG, Daniels R, Bhat CR, Heath RW (2016) Millimeter wave vehicular communication to support massive automotive sensing. IEEE Commun Mag 54(12):160–167

    Article  Google Scholar 

  4. Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98(September):27–42

    Article  Google Scholar 

  5. Deng DJ, Lien SY, Lin CC, Hung SC, Chen WB (2017) Latency control in software-defined mobile-edge vehicular networking. IEEE Commun Mag 55(8):87–93

    Article  Google Scholar 

  6. Va V, Shimizu T, Bansal G, Health RW (2016) Millimeter wave vehicular communications: a survey. Found Trends Netw 10(1):1–113

    Article  Google Scholar 

  7. Mabrouk A, Kobbane A, Sabir E, Ben-Othman J, Koutbi ME (2016) Meeting always-best-connected paradigm in heterogeneous vehicular network: a graph theory and a signaling game analysis. Veh Commun 5:1–8

    Google Scholar 

  8. He Z, Zhang D, Liang J (2016) Cost-efficient sensory data transmission in heterogeneous software-defined vehicular networks. IEEE Sens J 16(20):7342–7354

    Article  Google Scholar 

  9. Ahmad F, Franqueira VNL, Adnane A (2018) TEAM: a trust evaluation and management framework in context-enabled vehicular ad-hoc networks. IEEE Access 6(3):28643–28660

    Article  Google Scholar 

  10. Gai F, Zhang J, Zhu P, Jiang X (2017) Ratee-based trust management system for internet of vehicles. In: Wireless algorithms, systems, and applications. Lecture notes in computer science, vol 10251, pp 344–355

    Chapter  Google Scholar 

  11. Oubabas S, Aoudjit R, Rodrigues JJPC, Talbi S (2018) Secure and stable vehicular ad hoc network clustering algorithm based on hybrid mobility similarities and trust management scheme. Veh Commun 13:128–138

    Google Scholar 

  12. Sedjelmaci H, Senouci SM, Bouali T (2017) Predict and prevent from misbehaving intruders in heterogeneous vehicular networks. Veh Commun 10:74–83

    Google Scholar 

  13. Contreras J, Zeadally S, Guerrero-Ibanez JA (2017) Internet of vehicles: architecture, protocols, and security. IEEE Internet Things J 99:1–9

    Google Scholar 

  14. Lu N, Cheng N, Zhang N, Shen X, Mark JW (2014) Connected vehicles: solutions and challenges. IEEE Internet Things J 1(4):289–299

    Article  Google Scholar 

  15. Xu W et al (2018) Internet of vehicles in big data era. IEEE/CAA J Autom Sin 5(1):19–35

    Article  Google Scholar 

  16. Akhunzada A, Khan MK (2017) Toward secure software defined vehicular networks: taxonomy, requirements, and open issues. IEEE Commun Mag 55(7):110–118

    Article  Google Scholar 

  17. Maio A Di et al (2016) Enabling SDN in VANETs: what is impact on security? Sensors (Switzerland) 16(12):1–24

    Google Scholar 

  18. Cui L, Yu FR, Yan Q (2016) When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE Netw 30(1):58–65

    Article  Google Scholar 

  19. Hasrouny H, Samhat AE, Bassil C, Laouiti A (2017) VANet security challenges and solutions: a survey. Veh Commun 7:7–20

    Google Scholar 

  20. Singh J, Pasquier T, Bacon J, Ko H, Eyers D (2016) Twenty security considerations for cloud-supported internet of things. IEEE Internet Things J 3(3):269–284

    Article  Google Scholar 

  21. Wang L, Liu G, Sun L (2017) A secure and privacy-preserving navigation scheme using spatial crowdsourcing in fog-based VANETs. Sensors (Switzerland) 17(4)

    Article  Google Scholar 

  22. Mosqueda AG, Morales-Sandoval M, Villarreal-Reyes S, Galeana-Zapién H, Rodríguez RR, Alonso-Arévalo MÁ (2017) Multi-hop broadcast message dissemination in vehicular ad hoc networks: a security perspective review. Int J Distrib Sens Netw 13(11):1–21

    Google Scholar 

  23. Feng X, Li C-Y, Chen DX, Tang J (2017) A method for defensing against multi-source Sybil attacks in VANET. Peer-to-Peer Netw Appl 10(2):305–314

    Article  Google Scholar 

  24. Boeira F, Barcellos MP, Freitas EPD, Vinel A, Asplund M (2017) On the impact of Sybil attacks in cooperative driving scenarios. In: 2017 IFIP networking conference (IFIP networking) and workshops, pp 1–2

    Google Scholar 

  25. Sakiz F, Sen S (2017) A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV. Ad Hoc Netw 61:33–50

    Article  Google Scholar 

  26. Bouali T, Senouci S-M, Sedjelmaci H (2016) A distributed detection and prevention scheme from malicious nodes in vehicular networks. Int J Commun Syst 29:1683–1704

    Article  Google Scholar 

  27. Gai F, Zhang J, Zhu P, Jiang X (2017) Trust on the ratee: a trust management system for social internet of vehicles. Wirel Commun Mob Comput 2017

    Google Scholar 

  28. Raya M, Papadimitratos P, Gligor V, Hubaux J-P (2008) On data-centric trust establishment in ephemeral ad hoc networks. In: Procecedings of 27th IEEE conference on computer communications - INFOCOM, pp 1912–1920

    Google Scholar 

  29. Gurung S, Lin D, Squicciarini A, Bertino E (2013) Information-oriented trustworthiness evaluation in vehicular ad-hoc networks. Lecture notes in computer science, LNCS, vol 7873, pp 94–108

    Google Scholar 

  30. Jesudoss A, Raja SVK, Sulaiman A (2015) Stimulating truth-telling and cooperation among nodes in VANETs through payment and punishment scheme. Ad Hoc Netw 24(A):250–253

    Article  Google Scholar 

  31. Khan U, Agrawal S, Silakari S (2015) Detection of malicious nodes (DMN) in vehicular ad-hoc networks. Procedia - Procedia Comput Sci 46:965–972

    Article  Google Scholar 

  32. Haddadou N, Rachedi A, Ghamri-Doudane Y (2013) Trust and exclusion in vehicular ad hoc networks: an economic incentive model based approach. In: Proceedings of computing, communications and IT applications conference (ComComAp 2013), pp 13–18

    Google Scholar 

  33. Haddadou N, Rachedi A, Doudane YG (2015) A job market signaling scheme for incentive and trust management in vehicular ad hoc networks. IEEE Trans Veh Technol 64(8):3657–3674

    Article  Google Scholar 

  34. Yao X, Zhang X, Ning H, Li P (2017) Using trust model to ensure reliable data acquisition in VANETs. Ad Hoc Netw 55:107–118

    Article  Google Scholar 

  35. Hussain R, Nawaz W, Lee J, Son J, Seo JT (2016) A hybrid trust management framework for vehicular social networks. In: CSoNet 2016. LNCS, vol 9795, pp 214–225

    Chapter  Google Scholar 

  36. Sedjelmaci H, Senouci SM (2015) An accurate and efficient collaborative intrusion detection framework to secure vehicular networks. Comput Electr Eng 43:33–47

    Article  Google Scholar 

  37. Dhurandher SK, Obaidat MS, Jaiswal A, Tiwari A, Tyagi A (2010) Securing vehicular networks: a reputation and plausibility checks-based approach. In: 2010 IEEE Globecom work. GC’10, pp 1550–1554

    Google Scholar 

  38. Soleymani SA et al (2015) Trust management in vehicular ad hoc network: a systematic review. Eurasip J Wirel Commun Netw 146:1–22

    Google Scholar 

  39. Yang S, Li J, Liu Z, Wang S (2015) Managing trust for intelligence vehicles: a cluster consensus approach. In: IOV 2015. LNCS, vol 9502, pp 210–220

    Chapter  Google Scholar 

  40. Li W, Song H (2016) ART: an attack-resistant trust management scheme for securing vehicular ad hoc networks. IEEE Trans Intell Transp Syst 17(4):960–969

    Article  Google Scholar 

  41. Huang X, Yu R, Kang J, Zhang Y (2017) Distributed reputation management for secure and efficient vehicular edge computing and networks. IEEE Access 5:25408–25420

    Article  Google Scholar 

  42. Yang Z, Yang K, Lei L, Zheng K, Leung VCM (2018) Blockchain-based decentralized trust management in vehicular networks. IEEE Internet Things J, 1–10. https://doi.org/10.1109/jiot.2018.2836144

    Article  Google Scholar 

  43. Camacho F, Cárdenas C, Muñoz D (2017) Emerging technologies and research challenges for intelligent transportation systems: 5G, HetNets, and SDN. Int J Interact Des Manuf 12(1):327–335

    Article  Google Scholar 

  44. He Z, Cao J, Liu X (2016) SDVN: enabling rapid network innovation for heterogeneous vehicular communication. IEEE Netw 30(4):10–15

    Article  Google Scholar 

  45. Fontes RDR, Campolo C, Rothenberg CE, Molinaro A (2017) From theory to experimental evaluation: resource management in software-defined vehicular networks. IEEE Access 5:3069–3076

    Article  Google Scholar 

  46. Correia S, Boukerche A, Meneguette RI (2017) An architecture for hierarchical software-defined vehicular networks. IEEE Commun Mag 55(7):80–86

    Article  Google Scholar 

  47. Azizian M, Cherkaoui S, Hafid AS (2017) Vehicle software updates distribution with SDN and cloud computing. IEEE Commun Mag 55(8):74–79

    Article  Google Scholar 

  48. Wang K, Yin H, Quan W, Min G (2018) Enabling collaborative edge computing for software defined vehicular networks. IEEE Netw 32(5):112–117

    Article  Google Scholar 

  49. Mahmood A, Butler B, Jennings B (2018) Potential of augmented reality for intelligent transportation systems. In: Lee N (ed) Encyclopedia of computer graphics and games. Springer, Cham, pp 1–7

    Google Scholar 

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Acknowledgements

The corresponding author acknowledges the generous support of the Government of the Commonwealth of Australia for funding the research-at-hand via its International Research Training Program (Allocation No. 2017560).

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Authors and Affiliations

  1. Intelligent Computing Laboratory, Department of Computing, Macquarie University, Sydney, NSW, Australia

    Adnan Mahmood, Wei Emma Zhang, Quan Z. Sheng, Sarah Ali Siddiqui & Abdulwahab Aljubairy

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  1. Adnan Mahmood
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  2. Wei Emma Zhang
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  5. Abdulwahab Aljubairy
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Correspondence to Adnan Mahmood .

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  1. Northampton University, Northampton, UK

    Zaigham Mahmood

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Mahmood, A., Zhang, W.E., Sheng, Q.Z., Siddiqui, S.A., Aljubairy, A. (2019). Trust Management for Software-Defined Heterogeneous Vehicular Ad Hoc Networks . In: Mahmood, Z. (eds) Security, Privacy and Trust in the IoT Environment. Springer, Cham. https://doi.org/10.1007/978-3-030-18075-1_10

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  • DOI: https://doi.org/10.1007/978-3-030-18075-1_10

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