Skip to main content

Advertisement

SpringerLink
Log in

Mitigating the worst parent attack in RPL based internet of things

  • Published:
Cluster Computing Aims and scope Submit manuscript

A Correction to this article was published on 23 August 2022

This article has been updated

Abstract

The Low Power and Lossy Networks (LLNs) in the Internet of Things environment comprising constrained embedded devices have particular routing requirements that are well satisfied by the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). However, RPL is susceptible to several routing attacks. Worst Parent Attack (WPA) is an attack against RPL in which a malicious node intentionally chooses a sub-optimal path to the root node to forward its data packets. The result of which is sub-optimized performance and improper utilization of network resources of the IoT-LLNs. This paper proposes an efficient enhancement of the existing RPL protocol to make it resilient to the Worst Parent Attack. The proposed Enhanced RPL builds upon RPL and is henceforth named ERPL. The proposed ERPL achieves its objective by reducing the candidate set of parent nodes to an optimal parent set in the topological construction process. Thus, ERPL ensures that nodes choose a parent from a set of optimal nodes and makes IoT-LLNs resilient to WPA. We compare ERPL and RPL under normal and WPA scenarios. The comparison proves that ERPL, apart from providing security against the Worst Parent Attacks, also outperforms RPL in terms of energy consumption, packet delivery ratio, network convergence, and overall network overhead.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The data used in this research work is generated from the Cooja simulator available in Contiki Operating System, which is open source and publicly available. The method used for data generation is explained in the papers.

Change history

References

  1. Dohler, M., Barthel, D., Watteyne, T., Winter, T.: Routing requirements for urban low-power and lossy networks. In: RFC 5548 2009 May

  2. Heidari, A., Jabraeil Jamali, M.A., Jafari Navimipour, N., Akbarpour, S.: Internet of things offloading: ongoing issues, opportunities, and future challenges. Int. J. Commun. Syst. 33(14), e4474 (2020)

    Article  Google Scholar 

  3. Sadrishojaei, M., Navimipour, N.J., Reshadi, M., Hosseinzadeh, M.: A new preventive routing method based on clustering and location prediction in the mobile internet of things. IEEE Internet Things J. 8, 10652–10664 (2021)

    Article  Google Scholar 

  4. Sefati, S.S., Navimipour, N.J.: A QoS-aware service composition mechanism in the Internet of things using a hidden Markov model-based optimization algorithm. IEEE Internet Things J. 8, 15620 (2021)

    Article  Google Scholar 

  5. Winter, T., Thubert, P., Brandt, A., Hui, J., Kelsey, R., Pister, K., Struik, R., Vasseur, J.P., Alexander, R.: RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks, In RFC 6550, March (2012)

  6. Kim, H.-S., Ko, J., Culler, D.E., Paek, J.: Challenging the IPv6 routing protocol for low-power and lossy networks (RPL): a survey. IEEE Commun. Surv. Tutor. 19(4), 2502–25 (2017)

    Article  Google Scholar 

  7. Almusaylim, Z.A., Alhumam, A., Jhanjhi, N.Z.: Proposing a secure RPL based internet of things routing protocol: a review. Elsevier Ad Hoc Netw. 101, 102096 (2020)

    Article  Google Scholar 

  8. Mayzaud, A., Badonnel, R., Chrisment, I.: A taxonomy of attacks in RPL-based internet of things. Int. J. Netw. Secur. 18(3), 459–473 (2016)

    Google Scholar 

  9. Neerugatti, V., Reddy, A.R.: Artificial intelligence-based technique for detection of selective forwarding attack in rpl-based internet of things networks. In: Emerging Research in Data Engineering Systems and Computer Communications, pp. 67–77. Springer, Singapore (2019)

    Google Scholar 

  10. Pu, C.: Sybil attack in RPL-based internet of things: analysis and defenses. IEEE Internet Things J. 7(6), 4937–49 (2020)

    Article  Google Scholar 

  11. Agiollo, A., Conti, M., Kaliyar, P., Lin, T., Pajola, L.: DETONAR: detection of routing attacks in RPL-based IoT. IEEE Trans. Netw. Serv. Manag. 18, 1178 (2021)

    Article  Google Scholar 

  12. Ghahramani, M., Javidan, R., Shojafar, M., Taheri, R., Alazab, M., Tafazolli, R.: RSS: an energy-efficient approach for securing IoT service protocols against the DoS attack. IEEE Internet Things J. 8(5), 3619–35 (2020)

    Article  Google Scholar 

  13. Sahay, R., Geethakumari, G., Modugu, K.: Attack graph—based vulnerability assessment of rank property in RPL-6LOWPAN in IoT, IEEE 4th World Forum on Internet of Things (WF-IoT), pp. 308–313, 5–8 February (2018)

  14. Kamble, A., Malemath, V.S., Patil, D.: Security attacks and secure routing protocols in RPL-based Internet of Things: Survey. In: IEEE 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI), pp. 33–39, 3 February (2017)

  15. Kharrufa, H., Al-Kashoash, H.A., Kemp, A.H.: RPL-based routing protocols in IoT applications: a review. IEEE Sens. J. 19(15), 5952–67 (2019)

    Article  Google Scholar 

  16. Kritsis, K., Papadopoulos, G.Z., Gallais, A., Chatzimisios, P., Theoleyre, F.: A tutorial on performance evaluation and validation methodology for low-power and lossy networks. IEEE Commun. Surv. Tutor. 20(3), 1799–825 (2018)

    Article  Google Scholar 

  17. Ghaleb, B., Al-Dubai, A.Y., Ekonomou, E., Alsarhan, A., Nasser, Y., Mackenzie, L.M., Boukerche, A.: A survey of limitations and enhancements of the ipv6 routing protocol for low-power and lossy networks: a focus on core operations. IEEE Commun. Surv. Tutor. 21(2), 1607–35 (2018)

    Article  Google Scholar 

  18. Kamgueu, P.O., Nataf, E., Ndie, T.D.: Survey on RPL enhancements: a focus on topology, security and mobility. Elsevier Comput. Commun. 120, 10–21 (2018)

    Article  Google Scholar 

  19. Le, A., Loo, J., Lasebae, A., Vinel, A., Chen, Y., Chai, M.: The impact of rank attack on network topology of routing protocol for low-power and lossy networks. IEEE Sens. J. 13(10), 3685–92 (2013)

    Article  Google Scholar 

  20. Raoof, A., Matrawy, A., Lung, C.-H.: Routing attacks and mitigation methods for RPL-based internet of things. IEEE Commun. Surv. Tutor. 21(2), 1582–1606 (2018)

    Article  Google Scholar 

  21. Levis, P., Clausen, T., Hui, J., Gnawali, O., Ko, J.: The Trickle Algorithm. In: RFC 6206, March (2011)

  22. Rai, KK., Asawa, K.: Impact analysis of rank attack with spoofed IP on routing in 6LoWPAN network, In IEEE 2017 10th International Conference on Contemporary Computing (IC3), pp. 1–5, 10 Aug (2017)

  23. Semedo, F., Moradpoor, N., Rafiq, M.: Vulnerability assessment of objective function of RPL protocol for Internet of Things. In: Proceedings of the ACM 11th International Conference on Security of Information and Networks, pp. 1–6, Sep (2018)

  24. Sahay, R., Geethakumari, G., Modugu, K., Mitra, B.: Detection of misappropriation attacks in RPL-6LOWPAN in IoT using neural networks. In: IEEE 2018 Symposium Series in Computational Intelligence, pp. 1715–1722, 18–21 Nov (2018)

  25. Seth, AD., Biswas, S., Dhar, AK.: Detection and verification of decreased rank attack using round-trip times in RPL-based 6LoWPAN networks. In: IEEE 2020 International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1–6, 14 Dec (2020)

  26. Said, A.M, Yahyaoui, A., Yaakoubi, F., Abdellatif, T.: Machine learning based rank attack detection for smart hospital infrastructure. In: Springer 2020 International Conference on Smart Homes and Health Telematics, pp. 28–40, 24 June (2020)

  27. Airehrour, D., Gutierrez, J.A., Ray, S.K.: SecTrust-RPL: a secure trust-aware RPL routing protocol for Internet of Things. Elsevier Future Gen. Comput. Syst. 93, 860–76 (2019)

    Article  Google Scholar 

  28. Zaminkar, M., Fotohi, R.: SoS-RPL: securing internet of things against sinkhole attack using RPL protocol-based node rating and ranking mechanism. Springer Wirel. Personal Commun. 114, 1287–312 (2020)

    Article  Google Scholar 

  29. Prathapchandran, K., Janani, T.: A trust aware security mechanism to detect sinkhole attack in RPL-based IoT environment using random forest-RFTRUST. Elsevier Comput. Netw. 198, 108413 (2021)

    Article  Google Scholar 

  30. Glissa, G., Rachedi, A., Meddeb, A.: A secure routing protocol based on RPL for Internet of Things. In: IEEE 2016 Global Communications Conference (GLOBECOM), pp. 1–7, 4 Dec (2016)

  31. Dvir, A., Buttyan, L.: VeRA-version number and rank authentication in rpl, IEEE 8th International Conference on Mobile Adhoc and Sensor Systems (MASS), pp. 709–714, 17 Oct (2011)

  32. Perrey, H., Landsmann, M., Ugus, O.man, Schmidt, T.C., Wählisch, M.: TRAIL: topology authentication in RPL. arXiv preprint arXiv:1312.0984 (2013)

  33. Raoof, A., Lung, CH., Matrawy, A.: Introducing network coding to RPL: the chained secure mode (CSM). In; IEEE 2020 19th International Symposium on Network Computing and Applications (NCA), pp. 1–4, 24 Nov (2020)

  34. Karmakar, S., Sengupta, J., Bit, S.D.: LEADER: low overhead rank attack detection for securing RPL based IoT. In: IEEE International Conference on COMmunication Systems & NETworkS (COMSNETS), pp. 429–437, Jan (2021)

  35. Sahay, R., Geethakumari, G., Mitra, B.: A feedforward neural network based model to predict sub-optimal path attack in IoT-LLNs. In: IEEE 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp.400–409, 11 May (2020)

  36. Canbalaban, E., Sen, S.: A cross-layer intrusion detection system for RPL-based internet of things. In: Proceeding of the Springer International Conference on Ad-Hoc Networks and Wireless, pp. 214–227, 19 Oct (2020)

  37. Djedjig, N., Tandjaoui, D., Medjek, F., Romdhani, I.: Trust-aware and cooperative routing protocol for IoT security. Elsevier J. Inf. Secur. Appl. 52, 102467 (2020)

    Google Scholar 

  38. Preeth, S.S., Dhanalakshmi, R., Kumar, R., Si, S.: Efficient parent selection for RPL using ACO and coverage based dynamic trickle techniques. J. Ambient Intell. Hum. Comput. 31, 1–5 (2019)

    Google Scholar 

  39. Royaee, Z., Mirvaziri, H., Bardsiri, A.K.: Designing a context-aware model for RPL load balancing of low power and lossy networks in the internet of things. Springer J. Ambient Intell. Hum. Comput. 12(2), 2449–68 (2021)

    Article  Google Scholar 

  40. Le, A., Loo, J., Chai, K.K., Aiash, M.: A specification-based IDS for detecting attacks on RPL-based network topology. Information 7(2), 25 (2016)

    Article  Google Scholar 

  41. Simoglou, G., Violettas, G., Petridou, S., Mamatas, L.: Intrusion detection systems for RPL security: a comparative analysis. Elsevier Comput. Secur. 104, 102219 (2021)

    Article  Google Scholar 

  42. Lamaazi, H., Benamar, N.: A comprehensive survey on enhancements and limitations of the RPL protocol: a focus on the objective function. Elsevier Ad Hoc Netw. 96, 102001 (2020)

    Article  Google Scholar 

  43. Zikria, Y.B., Afzal, M.K., Ishmanov, F., Kim, S.W., Yu, H.: A survey on routing protocols supported by the Contiki Internet of things operating system. Elsevier Future Gen. Comput. Syst. 82, 200–19 (2018)

    Article  Google Scholar 

  44. Clark, B.N., Colbourn, C.J., Johnson, D.S., Unit disk graphs: Unit disk graph. Discret. Math. 86, 165–177 (1990)

    Article  MathSciNet  Google Scholar 

  45. Tsvetkov, T., Klein, A.: RPL: IPv6 routing protocol for low power and lossy networks. Network 59, 59–66 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, IcfaiTech (Faculty of Science and Technology), IFHE, Hyderabad, India

    Rashmi Sahay

  2. Department of Computer Science and Information Systems, BITS Pilani Hyderabad Campus, Hyderabad, India

    G. Geethakumari & Barsha Mitra

Authors
  1. Rashmi Sahay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Geethakumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Barsha Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RS conceptualized, developed, and executed the idea. GG verified the idea. RS wrote the manuscript. The manuscript was reviewed, edited, and finalized by GG, RS and BM.

Corresponding author

Correspondence to Rashmi Sahay.

Ethics declarations

Funding

This research is not funded.

Conflict of interest

The authors declare no conflict of Interest.

Code availability

Code will be made available on request.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: The authors G. Geethakumari and Barsha Mitra affiliation has been corrected.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahay, R., Geethakumari, G. & Mitra, B. Mitigating the worst parent attack in RPL based internet of things. Cluster Comput 25, 1303–1320 (2022). https://doi.org/10.1007/s10586-021-03528-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03528-5

Keywords

Search

Navigation