Skip to main content
SpringerLink
Log in
Book cover

International Conference on Ad-Hoc Networks and Wireless

ADHOC-NOW 2020: Ad-Hoc, Mobile, and Wireless Networks pp 214–227Cite as

A Cross-Layer Intrusion Detection System for RPL-Based Internet of Things

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12338))

Abstract

The Internet of Things (IoT) is a heterogeneous network of constrained devices connected both to each other and to the Internet. Since the significance of IoT has risen remarkably in recent years, a considerable amount of research has been conducted in this area, and especially on, new mechanisms and protocols suited to such complex systems. Routing Procotol for Lower-Power and Lossy Networks (RPL) is one of the well-accepted routing protocols for IoT. Even though RPL has defined some specifications for its security, it is still vulnerable to insider attacks. Moreover, lossy communication links and resource-constraints of devices introduce a challenge for developing suitable security solutions for such networks. Therefore, in this study, a new intrusion detection system based on neural networks is proposed for detecting specific attacks against RPL. Besides features collected from the routing layer, the effects of link layer-based features are investigated on intrusion detection. To the best of our knowledge, this study presents the first cross-layer intrusion detection system in the literature.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://wise.cs.hacettepe.edu.tr/projects/rplsec/.

References

  1. IoT: Number of Connected Devices Worldwide 2012–2025|statistica. https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/. Accessed 18 Jan 2020

  2. Tmote sky from moteiv. https://insense.cs.st-andrews.ac.uk/files/2013/04/tmote-sky-datasheet.pdf. Accessed 13 Jan 2020

  3. Airehrour, D., Gutierrez, J.A., Ray, S.K.: Sectrust-RPL: a secure trust-aware RPL routing protocol for Internet of Things. Future Gener. Comput. Syst. 93, 860–876 (2019). https://doi.org/10.1016/j.future.2018.03.021. http://www.sciencedirect.com/science/article/pii/S0167739X17306581

    Article  Google Scholar 

  4. Alexander, R., et al.: RPL: IPv6 routing protocol for low-power and lossy networks. RFC 6550 (March 2012). https://doi.org/10.17487/RFC6550, https://rfc-editor.org/rfc/rfc6550.txt

  5. Aris, A., Oktug, S.F., Berna Ors Yalcin, S.: RPL version number attacks: in-depth study. In: NOMS 2016–2016 IEEE/IFIP Network Operations and Management Symposium, pp. 776–779 (2016)

    Google Scholar 

  6. Arş, A., Örs Yalçın, S.B., Oktuğ, S.F.: New lightweight mitigation techniques for RPL version number attacks. Ad Hoc Netw. 85, 81–91 (2019). https://doi.org/10.1016/j.adhoc.2018.10.022. http://www.sciencedirect.com/science/article/pii/S1570870518307625

    Article  Google Scholar 

  7. Aydogan, E., Yilmaz, S., Sen, S., Butun, I., Forsström, S., Gidlund, M.: A central intrusion detection system for RPL-based industrial internet of things. In: 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS), pp. 1–5 (2019)

    Google Scholar 

  8. Chollet, F., et al.: Keras. https://keras.io (2015)

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

    Google Scholar 

  10. 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), 2502–2525 (2017)

    Google Scholar 

  11. Le, A., Loo, J., Chai, M., Aiash, M.: A specification-based IDS for detecting attacks on RPL-based network topology. Information 7, 25 (2016). https://doi.org/10.3390/info7020025

    Article  Google Scholar 

  12. 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, 3685–3692 (2013). https://doi.org/10.1109/JSEN.2013.2266399

    Article  Google Scholar 

  13. Le, A., Loo, J., Luo, Y., Lasebae, A.: The impacts of internal threats towards routing protocol for low power and lossy network performance, pp. 000789–000794 (July 2013). https://doi.org/10.1109/ISCC.2013.6755045

  14. Levis, P., Clausen, T.H., Gnawali, O., Hui, J., Ko, J.: The trickle algorithm. RFC 6206 (March 2011). https://doi.org/10.17487/RFC6206, https://rfc-editor.org/rfc/rfc6206.txt

  15. Maple, C.: Security and privacy in the Internet of Things. J. Cyber Policy 2, 155–184 (2017). https://doi.org/10.1080/23738871.2017.1366536

    Article  Google Scholar 

  16. Mayzaud, A., Badonnel, R., Chrisment, I.: A distributed monitoring strategy for detecting version number attacks in RPL-based networks. IEEE Trans. Netw. Serv. Manag. 14(2), 472–486 (2017). https://doi.org/10.1109/TNSM.2017.2705290

    Article  Google Scholar 

  17. Mayzaud, A., Sehgal, A., Badonnel, R., Chrisment, I., Schönwälder, J.: A study of RPL DODAG version attacks. In: Sperotto, A., Doyen, G., Latré, S., Charalambides, M., Stiller, B. (eds.) AIMS 2014. LNCS, vol. 8508, pp. 92–104. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43862-6_12

    Chapter  Google Scholar 

  18. McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, Austin, TX, vol. 445, pp. 51–56 (2010)

    Google Scholar 

  19. Müller, N.M., Debus, P., Kowatsch, D., Böttinger, K.: Distributed anomaly detection of single mote attacks in RPL networks. In: Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, SECRYPT, vol. 2, pp. 378–385. INSTICC, SciTePress (2019). https://doi.org/10.5220/0007836003780385

  20. Napiah, M.N., Bin Idris, M.Y.I., Ramli, R., Ahmedy, I.: Compression header analyzer intrusion detection system (CHA - IDS) for 6LoWPAN communication protocol. IEEE Access 6, 16623–16638 (2018)

    Google Scholar 

  21. Oliphant, T.E.: A Guide to NumPy, vol. 1. Trelgol Publishing, USA (2006)

    Google Scholar 

  22. Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., Voigt, T.: Cross-level sensor network simulation with cooja. In: Annual IEEE Conference on Local Computer Networks, pp. 641–648 (November 2006). https://doi.org/10.1109/LCN.2006.322172

  23. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  24. Pongle, P.: Real time intrusion and wormhole attack detection in Internet of Things. Int. J. Comput. Appl. 121, 1–9 (2015). https://doi.org/10.5120/21565-4589

    Article  Google Scholar 

  25. Ray, P.: A survey on Internet of Things architectures. J. King Saud Univ. Comput. Inf. Sci. 30(3), 291–319 (2018). https://doi.org/10.1016/j.jksuci.2016.10.003. http://www.sciencedirect.com/science/article/pii/S1319157816300799

    Article  Google Scholar 

  26. Raza, S., Wallgren, L., Voigt, T.: Svelte: real-time intrusion detection in the Internet of Things. Ad Hoc Networks 11(8), 2661–2674 (2013). https://doi.org/10.1016/j.adhoc.2013.04.014. http://www.sciencedirect.com/science/article/pii/S1570870513001005

    Article  Google Scholar 

  27. Verma, A., Ranga, V.: Security of RPL based 6LoWPAN networks in the Internet of Things: a review. IEEE Sens. J. 20(11), 5666–5690 (2020)

    Google Scholar 

  28. Verma, A., Ranga, V.: Mitigation of dis flooding attacks in RPL-based 6LoWPAN networks. Trans. Emerg. Telecommun. Technol. 31(2), e3802 (2020). https://doi.org/10.1002/ett.3802. https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.3802

    Article  Google Scholar 

  29. Yavuz, F.Y., Unal, D., Gul, E.: Deep learning for detection of routing attacks in the Internet of Things. Int. J. Comput. Intell. Syst. 12, 39–58 (2018). https://doi.org/10.2991/ijcis.2018.25905181

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to Emre Aydogan and Selim Yilmaz for sharing their experiences during the feature selection and feature extraction process for our neural-network based intrusion detection system.

Author information

Authors and Affiliations

  1. WISE Lab., Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Erdem Canbalaban & Sevil Sen

Authors
  1. Erdem Canbalaban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sevil Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Erdem Canbalaban or Sevil Sen .

Editor information

Editors and Affiliations

  1. DEI, Polytechnic University of Bari, Bari, Italy

    Luigi Alfredo Grieco

  2. DEI, Polytechnic University of Bari, Bari, Italy

    Gennaro Boggia

  3. DEI, Polytechnic University of Bari, Bari, Italy

    Giuseppe Piro

  4. Duquesne University, Pittsburgh, PA, USA

    Yaser Jararweh

  5. Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Claudia Campolo

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Canbalaban, E., Sen, S. (2020). A Cross-Layer Intrusion Detection System for RPL-Based Internet of Things. In: Grieco, L.A., Boggia, G., Piro, G., Jararweh, Y., Campolo, C. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science(), vol 12338. Springer, Cham. https://doi.org/10.1007/978-3-030-61746-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61746-2_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61745-5

  • Online ISBN: 978-3-030-61746-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation