Abstract
The Internet Engineering Task Force (IETF) introduced the RPL routing protocol to meet the routing requirements of the lossy networks. The inbuilt security features in the RPL design prevent external attacks but fail to mitigate internal attackers’ threats. The absence of robust security mechanisms in the RPL design has made it vulnerable to different categories of routing attacks. A comprehensive literature review is carried out in this study, which compares the mitigation approaches suggested by different authors in their research work. This paper aims to compare and evaluate different mitigation techniques in order to identify research trends, gaps, and opportunities. The existing research work is compared with one another in terms of the experimental setup, attack focus, proposed methodology, and performance metrics to determine the best approach for tackling the RPL routing attacks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kharrufa, H., Al-Kashoash, H.A.A., Kemp, A.H.: RPL-based routing protocols in IoT applications: a review. IEEE Sens. J. 19, 5952–5967 (2019). https://doi.org/10.1109/JSEN.2019.2910881
Aris, A., Oktug, S.F., Yalcin, S.B.O.: RPL version number attacks: in-depth study. In: IEEE/IFIP Network Operations and Management Symposium, pp. 776–779 (2016). https://doi.org/10.1109/NOMS.2016.7502897
Mayzaud, A., Badonnel, R., Chrisme, I.: A taxonomy of attacks in RPL-based internet of things. Int. J. Netw. Secur. 18, 459–473 (2016)
Kim, H., 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)
Hui, J., Vasseur, J.: The Routing Protocol for Low-Power and Lossy Networks (RPL) Option for Carrying RPL Information in Data-Plane Datagrams. Internet Requests for Comments (2012)
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
Dvir, A., Holczer, T., Buttyan, L.: VeRA - version number and rank authentication in RPL. In: IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems (2011)
Perrey, H., Landsmann, M., Ugus, O., Schmidt, T.C.: TRAIL: topology authentication in RPL. In: Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks, pp. 59–64 (2016)
Neerugatti, V., Mohan Reddy, A.R.: Machine learning based technique for detection of rank attack in RPL based internet of things networks. IJITEE 8 (2019)
Le, A., Loo, J., Luo, Y., Lasebae, A.: Specification-based IDS for securing RPL from topology attacks. In: IFIP Wireless Days (WD), pp. 1–3 (2011)
Airehrour, D., Gutierrez, J.A., Ray, S.K.: SecTrust-RPL: a secure trust-aware RPL routing protocol for Internet of Things. Futur. Gener. Comput. Syst. 93, 860–876 (2019)
Groves, B., Pu, C.: A gini index-based countermeasure against sybil attack in the internet of things. In: IEEE Military Communications Conference (MILCOM), pp. 1–6 (2019)
Ribera, E.G., Alvarez, B.M., Samuel, C., Ioulianou, P.P., Vassilakis, V.G.: Heartbeat-based detection of blackhole and greyhole attacks in RPL networks. In: 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), pp. 1–6 (2020)
Airehrour, D., Gutierrez, J., Ray, S.K.: Securing RPL routing protocol from blackhole attacks using a trust-based mechanism. In: 26th International Telecommunication Networks and Applications Conference (ITNAC), pp. 115–120 (2016). https://doi.org/10.1109/ATNAC.2016.7878793
Jiang, J., Liu, Y., Dezfouli, B.: A root-based defense mechanism against RPL blackhole attacks in internet of things networks. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1194–1199 (2018). https://doi.org/10.23919/APSIPA.2018.8659504
Verma, A., Ranga, V.: ELNIDS: ensemble learning based network intrusion detection system for RPL based internet of things. In: 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), pp. 1–6 (2019). https://doi.org/10.1109/IoT-SIU.2019.8777504
Sharma, M., Elmiligi, H., Gebali, F., Verma, A.: Simulating attacks for RPL and generating multi-class dataset for supervised machine learning. In: IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 20–26 (2019). https://doi.org/10.1109/IEMCON.2019.8936142
Liu, J., Kantarci, B., Adams, C.: Machine learning-driven intrusion detection for contiki-NG-based IoT networks exposed to NSL-KDD dataset. In: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, pp. 25–30 (2020)
Cakir, S., Toklu, S., Yalcin, N.: RPL attack detection and prevention in the internet of things networks using a GRU based deep learning. IEEE Access 8, 183678–183689 (2020). https://doi.org/10.1109/ACCESS.2020.3029191
Shukla, P.: ML-IDS: a machine learning approach to detect wormhole attacks in Internet of Things. In: Intelligent Systems Conference (IntelliSys), pp. 234–240 (2017). https://doi.org/10.1109/IntelliSys.2017.8324298
Raza, S., Wallgren, L., Voigt, T.: SVELTE: real-time intrusion detection in the Internet of Things. Ad Hoc Netw. 11, 2661–2674 (2013). ISSN 1570-8705
Bostani, H., Sheikhan, M.: Hybrid of anomaly-based and specification-based IDS for internet of things using unsupervised OPF based on mapreduce approach. Comput. Commun. 98, 52–71 (2017). ISSN 0140-3664
Farzaneh, B., Montazeri, M.A., Jamali, S.: An anomaly-based IDS for detecting attacks in RPL-based internet of things. In: 5th International Conference on Web Research (ICWR), pp. 61–66 (2019). https://doi.org/10.1109/ICWR.2019.8765272
Abbou, A.N., Baddi, Y., Hasbi, A.: Routing over low power and lossy networks protocol: overview and performance evaluation. In: International Conference of Computer Science and Renewable Energies (ICCSRE), pp. 1–6 (2019). https://doi.org/10.1109/ICCSRE.2019.8807584
Foley, J., Moradpoor, N., Ochen, H.: Employing a machine learning approach to detect combined internet of things attacks against two objective functions using a novel dataset. In: Security and Communication Networks (2020)
Davis, A., Gill, S., Wong, R., Tayeb, S.: Feature selection for deep neural networks in cyber security applications. In: Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–7 (2020). https://doi.org/10.1109/IEMTRONICS51293.2020.9216403
Kareem, M.A., Tayeb, S.: ML-based NIDS to secure RPL from routing attacks. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), NV, USA, pp. 1000–1006 (2021). https://doi.org/10.1109/CCWC51732.2021.9375844
Tayeb, S., Latifi, S.: Will an emerging standard take over the routing realm? An evaluative analysis of DUAL and SPF. In: 2016 6th International Conference on IT Convergence and Security (ICITCS), Prague, Czech Republic, pp. 1–5 (2016). https://doi.org/10.1109/ICITCS.2016.7740380
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kareem, M.A., Tayeb, S. (2022). Securing Routing in Low Power and Lossy Networks. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1. FTC 2021. Lecture Notes in Networks and Systems, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-89906-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-89906-6_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89905-9
Online ISBN: 978-3-030-89906-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)