A Review of Isolation Attack Mitigation Mechanisms in RPLBased 6LoWPAN of Internet of Things

V.R. Rajasekar; S. Rajkumar

doi:10.24423/cames.2024.764

V.R. Rajasekar School of Computer Science and Engineering, Vellore Institute of Technology, India
S. Rajkumar School of Computer Science and Engineering, Vellore Institute of Technology, India

DOI: http://dx.doi.org/10.24423/cames.2024.764

Abstract

The Routing Protocol for Low-Power and Lossy Networks (RPL) is an open standard routing protocol defined by the Internet Engineering Task Force (IETF) to address the constraints of IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). RPL is susceptible to various attacks, including isolation attacks, in which a node or a set of RPL nodes can be isolated from the rest of the network. Three significant isolation attacks are the black hole attack (BHA), selective forwarding attack (SFA), and destination advertisement object (DAO) inconsistency attack (DAO-IA). In a BHA, a malicious node drops all packets intended for transmission silently. In an SFA, a malicious node forwards only selected packets and drops the other received packets. In a DAO-IA, a malicious node drops the received data packet and replies with a forwarding error packet, causing the parent node to discard valid downward routes from the routing table. We review the literature on proposed mechanisms, propose a taxonomy, and analyze the features, limitations, and performance metrics of existing mechanisms. Researchers primarily focus on power consumption as the key performance metric when mitigating BHA (47%), SFA (51%), and DAO-IA (100%), with downward latency being the least addressed metric for BHA (4%) and SFA (3%), and control packet overhead being the least addressed for DAO-IA (37%). Finally, we discuss the unresolved issues and research challenges in mitigating RPL isolation attacks.

Keywords

IoT, LLN, 6LoWPAN, isolation attacks, black hole, selective forwarding, DAO inconsistency.,

References

1. K. Kumar, A.K. Singh, S. Kumar, P. Sharma, J. Sharna, The role of dynamic network slicing in 5G: IoT and 5G mobile networks, [in:] Evolution of Software-Defined Networking Foundations for IoT and 5G Mobile Networks, S. Kumar, M.C. Trivedi, P. Rajan [Eds.], pp. 159–171, IGI Global, 2021, doi: 10.4018/978-1-7998-4685-7.ch009.
2. A. Colakovic, M. Hadžialic , Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues, Computer Networks, 144: 17–39, 2018, doi: 10.1016/j.comnet.2018.07.017.
3. N. Kushalnagar, G. Montenegro, C. Schumacher, RFC 4919 – IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs): Overview, Assumptions, Problem Statement, and Goals, Network Working Group, 2007, doi: 10.17487/RFC4919.
4. A. Musaddiq, Y. Bin Zikria, O. Hahm, H. Yu, A.K. Bashir, S.W. Kim, A survey on resource management in IoT operating systems, IEEE Access, 6: 8459–8482, 2018, doi: 10.1109/ACCESS.2018.2808324.
5. D. Sourailidis, R.-A. Koutsiamanis, G.Z. Papadopoulos, D. Barthel, N. Montavont, RFC 6550: On minimizing the control plane traffic of RPL-based industrial networks, [in:] IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks (WoWMoM)”, Cork, Ireland, pp. 439–444, 2020, doi: 10.1109/WoWMoM49955.2020.00080.
6. A. Agiollo, M. Conti, P. Kaliyar, T.N. Lin, L. Pajola, DETONAR: Detection of routing attacks in RPL-based IoT, IEEE Transactions on Network and Service Management, 18(2): 1178–1190, 2021, doi: 10.1109/TNSM.2021.3075496.
7. M.R. Palattella et al., Standardized protocol stack for the Internet of (important) Things, IEEE Communications Surveys & Tutorials, 15(3): 1389–1406, 2013, doi: 10.1109/ SURV.2012.111412.00158.
8. P.P. Ioulianou, V.G. Vassilakis, S.F. Shahandashti, A trust-based intrusion detection system for RPL networks: detecting a combination of rank and blackhole attacks, Journal of Cybersecurity and Privacy, 2(1): 124–153, 2022, doi: 10.3390/JCP2010009.
9. D.C. Mehetre, S.E. Roslin, S.J.Wagh, Detection and prevention of black hole and selective forwarding attack in clustered WSN with active trust, Cluster Computing, 22(Suppl. 1): 1313–1328, 2019, doi: 10.1007/S10586-017-1622-9/METRICS.
10. A.S. Baghani, S. Rahimpour, M. Khabbazian, The DAO induction attack against the RPLbased Internet of Things, [in:] 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, 17–19 September, pp. 1–5, 2020, doi: 10.23919/SOFTCOM50211.2020.9238224.
11. A. Mayzaud, R. Badonnel, I. Chrisment, A taxonomy of attacks in RPL-based Internet of Things, International Journal of Network Security, 18(3): 459–473, 2016, doi: 10.6633/ IJNS.201605.18(3).07.
12. A. Verma, V. Ranga, Security of RPL based 6LoWPAN networks in the Internet of Things: A review, IEEE Sensor Journal, 20(11): 5666–5690, 2020, doi: 10.1109/JSEN.2020.2973677.
13. S.M. Muzammal, R.K. Murugesan, N.Z. Jhanjhi, A comprehensive review on secure routing in Internet of Things: Mitigation methods and trust-based approaches, IEEE Internet of Things Journal, 8(6): 4186–4210, 2021, doi: 10.1109/JIOT.2020.3031162.
14. J. Granjal, E. Monteiro, J. Sa Silva, Security for the Internet of Things: A survey of existing protocols and open research issues, IEEE Communications Surveys & Tutorials, 17(3): 1294–1312, 2015, doi: 10.1109/COMST.2015.2388550.
15. P. Pongle, G. Chavan, A survey: Attacks on RPL and 6LoWPAN in IoT, [in:] 2015 International Conference on Pervasive Computing (ICPC), Pune, India, pp. 1–6, 2015, doi: 10.1109/PERVASIVE.2015.7087034.
16. R. Chauhan, S. Kumar, Packet loss prediction using artificial intelligence unified with big data analytics, internet of things and cloud computing technologies, [in:] 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, pp. 01–06, 2021, doi: 10.1109/ISCON52037.2021.9702517.
17. A. Liberati et al., The PRISMA statement for reporting systematic reviews and metaanalyses of studies that evaluate health care interventions: Explanation and elaboration, Journal of Clinical Epidemiology, 62(10): e1–e34, 2009, doi: 10.1016/j.jclinepi.2009.06.006.
18. A. Mathur, T. Newe, M. Rao, Defence against black hole and selective forwarding attacks for medical WSNs in the IoT, Sensors, 16(1): 118, 2016, doi: 10.3390/S16010118.
19. T. Zhang, T. Zhang, X. Ji, W. Xu, Cuckoo-RPL: Cuckoo filter based RPL for defending AMI network from blackhole attacks, [in:] 2019 Chinese Control Conference (CCC), Guangzhou, China, pp. 8920–8925, 2019, doi: 10.23919/ChiCC.2019.8866139.
20. J. Jiang, Y. Liu, B. Dezfouli, A root-based defense mechanism against RPL blackhole attacks in Internet of Things networks, [in:] 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA, pp. 1194–1199, 2018, doi: 10.23919/APSIPA.2018.8659504.
21. N. Bhalaji, K.S. Hariharasudan, K. Aashika, A trust based mechanism to combat blackhole attack in RPL protocol, [in:] ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, V. Gunjan, V. Garcia Diaz, M. Cardona, V. Solanki, K. Sunitha [Eds.], pp. 457–464, Springer, Singapore, 2019, doi: 10.1007/978-981-13-8461-5_51.
22. D. Airehrour, J. Gutierrez, S.K. Ray, A trust-aware RPL routing protocol to detect blackhole and selective forwarding attacks, Journal of Telecommunications and the Digital Economy, 5(1): 50–69, 2017, doi: 10.18080/ajtde.v5n1.88.
23. V. Kiran, S. Rani, P. Singh, Towards a light weight routing security in IoT using noncooperative game models and Dempster–Shaffer theory, Wireless Personal Communications, 110(4): 1729–1749, 2020, doi: 10.1007/S11277-019-06809-W.
24. S.Y. Hashemi, F. Shams Aliee, Dynamic and comprehensive trust model for IoT and its integration into RPL, Journal of Supercomputing, 75(7): 3555–3584, 2019, doi: 10.1007/S11227-018-2700-3.
25. T. Sakthivel, R.M. Chandrasekaran, A dummy packet-based hybrid security framework for mitigating routing misbehavior in multi-hop wireless networks, Wireless Personal Communications, 101(3): 1581–1618, 2018, doi: 10.1007/S11277-018-5778-2.
26. S.M. Muzammal, R.K. Murugesan, N.Z. Jhanjhi, L.T. Jung, SMTrust: Proposing trustbased secure routing protocol for RPL attacks for IoT applications, [in:] 2020 International Conference on Computational Intelligence (ICCI), Bandar Seri Iskandar, Malaysia, pp. 305–310, 2020, doi: 10.1109/ICCI51257.2020.9247818.
27. S. Zangeneh, R. Roustaei, A novel approach for protecting RPL routing protocol against blackhole attacks in IoT networks, PREPRINT (Ver. 1) available at Research Square, 2021, doi: 10.21203/rs.3.rs-174724/v1.
28. R. Mehta, M.M. Parmar, Trust based mechanism for Securing IoT Routing Protocol RPL against Wormhole and Grayhole Attacks, [in:] 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, India, pp. 1–6, 2018, doi: 10.1109/I2CT.2018.8529426.
29. J. Jiang, Y. Liu, Secure IoT routing: Selective forwarding attacks and trust-based defenses in RPL network, arXiv, 2022, doi: 10.48550/arxiv.2201.06937.
30. S. Suhail, S.R. Pandey, C.S. Hong, Detection of selective forwarding attack in RPL-based Internet of Things through provenance, [in:] Proceedings of the 2018 Korean Software Conference (KSC2018), Pyeongchang, South Korea, Dec. 19, 2018, pp. 965–967, Korean Society of Information Scientists and Engineers Academic, 2018.
31. S. Suhail, S.R. Pandey, C.S. Hong, Using provenance to detect selective forwarding attack in RPL-based Internet of Things, Journal of Information Science and Computing Practices, 26(1): 20–25, 2020, doi: 10.5626/KTCP.2020.26.1.20.
32. F. Ahmed, Y.B. Ko, Mitigation of black hole attacks in routing protocol for low power and lossy networks, Security and Communication Networks, 9(18): 5143–5154, 2016, doi: 10.1002/sec.1684.
33. V. Neerugatti, A.R.M. Reddy, Detection and prevention of black hole attack in RPL Protocol based on the threshold value of nodes in the Internet of Things networks, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(9S3): 325–329, 2019, doi: 10.35940/ijitee.I3060.0789S319.
34. B. Ghaleb, A. Al-Dubai, E. Ekonomou, M. Qasem, I. Romdhani, L. Mackenzie, Addressing the DAO insider attack in RPL’s Internet of Things networks, IEEE Communications Letters, 23(1): 68–71, 2019, doi: 10.1109/LCOMM.2018.2878151.
35. C. Pu, Mitigating DAO inconsistency attack in RPL-based low power and lossy networks, [in:] 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, pp. 570–574, 2018, doi: 10.1109/CCWC.2018.8301614.
36. I. Wadhaj, B. Ghaleb, C. Thomson, A. Al-Dubai, W.J. Buchanan, Mitigation mechanisms against the DAO attack on the routing protocol for low power and lossy networks (RPL), IEEE Access, 8: 43665–43675, 2020, doi: 10.1109/ACCESS.2020.2977476.
37. R. Sahay, G. Geethakumari, B. Mitra, V. Thejas, Exponential smoothing based approach for detection of blackhole attacks in IoT, [in:] 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Indore, India, Vol. 2018, 2018, doi: 10.1109/ANTS.2018.8710073.
38. D. Airehrour, J. Gutierrez, S.K. Ray, Securing RPL routing protocol from blackhole attacks using a trust-based mechanism, [in:] 2016 26th International Telecommunication Networks and Applications Conference (ITNAC), Dunedin, New Zealand, pp. 115–120, 2016, doi: 10.1109/ATNAC.2016.7878793.
39. G. Glissa, A. Rachedi, A. Meddeb, A secure routing protocol based on RPL for Internet of Things, [in:] 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, pp. 1–7, 2016, doi: 10.1109/GLOCOM.2016.7841543.
40. H.B. Patel, D.C. Jinwala, Blackhole detection in 6LoWPAN based Internet of Things: An anomaly based approach, [in:] TENCON 2019 – 2019 IEEE Region 10 Conference (TENCON), Kochi, India, pp. 947–954, 2019, doi: 10.1109/TENCON.2019.8929491.
41. S. Luangoudom, D. Tran, T. Nguyen, H.A. Tran, G. Nguyen, Q.T. Ha, svBLOCK: Mitigating black hole attack in low-power and lossy networks, International Journal of Sensor Networks, 32(2): 77–86, 2020, doi: 10.1504/IJSNET.2020.104923.
42. F. Gara, L. Ben Saad, R. Ben Ayed, An intrusion detection system for selective forwarding attack in IPv6-based mobile WSNs, [in:] 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain, pp. 276–281, 2017, doi: 10.1109/IWCMC.2017.7986299.
43. S. Raza, L. Wallgren, T. Voigt, SVELTE: Real-time intrusion detection in the Internet of Things, Ad Hoc Networks, 11(8): 2661–2674, 2013, doi: 10.1016/j.adhoc.2013.04.014.
44. G. Soni, R. Sudhakar, A L-IDS against dropping attack to secure and improve RPL performance in WSN aided IoT, [in:] 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, pp. 377–383, 2020, doi: 10.1109/SPIN48934.2020.9071118.
45. E.G. Ribera, B. Martinez Alvarez, C. Samuel, P.P. Ioulianou, V.G. Vassilakis, Heartbeatbased detection of blackhole and greyhole attacks in RPL networks, [in:] 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal, pp. 1–6, 2020, doi: 10.1109/CSNDSP49049.2020.9249519.
46. L. Wallgren, S. Raza, T. Voigt, Routing attacks and countermeasures in the RPL-based Internet of Things, International Journal of Distributed Sensor Networks, 9(8): 794326, 2013, doi: 10.1155/2013/794326.
47. K.N. Ambili, J. Jose, TN-IDS for network layer attacks in RPL based IoT systems, Cryptology ePrint Archive, 2020: 1094, 2020, https://ia.cr/2020/1094.
48. A. Lahbib, K. Toumi, S. Elleuch, A. Laouiti, S. Martin, Link reliable and trust aware RPL routing protocol for Internet of Things, [in:] 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA, pp. 1–5, 2017, doi: 10.1109/NCA.2017.8171360.
49. A.L. Santos, C.A.V. Cervantes, M. Nogueira, B. Kantarci, Clustering and reliability-driven mitigation of routing attacks in massive IoT systems, Journal of Internet Services and Applications, 10(1): 18, 2019, doi: 10.1186/S13174-019-0117-8.
50. Z.A. Khan, P. Herrmann, A trust based distributed intrusion detection mechanism for Internet of Things, [in:] 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), Taipei, Taiwan, pp. 1169–1176, 2017, doi: 10.1109/AINA.2017.161.
51. F. Gara, L. Ben Saad, R. Ben Ayed, An efficient intrusion detection system for selective forwarding and clone attackers in IPv6-based wireless sensor networks under mobility, International Journal on Semantic Web and Information Systems, 13(3): 22–47, 2017, doi: 10.4018/IJSWIS.2017070102.
52. H. Bostani, M. Sheikhan, Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach, Computer Communications, 98: 52–71, 2017, doi: 10.1016/j.comcom.2016.12.001.
53. H.B. Patel, D.C. Jinwala, 6MID: Mircochain based intrusion detection for 6LoWPAN based IoT networks, Procedia Computer Science, 184: 929–934, 2021, doi: 10.1016/J.PROCS.2021.04.023.
54. R. Stephen, L. Arockiam, E2V: Techniques for detecting and mitigating rank inconsistency attack (RInA) in RPL based Internet of Things, Journal of Physics: Conference Series, 1142(1): 012009, 2018, doi: 10.1088/1742-6596/1142/1/012009.
55. H.B. Patel, D.C. Jinwala, Trust and strainer based approach for mitigating blackhole attack in 6LowPAN: A hybrid approach, International Journal of Computer Science, 48(4): 1062, 2021, https://www.iaeng.org/IJCS/issues_v48/issue_4/IJCS_48_4_25.pdf.
56. R. Sahay, G. Geethakumari, B. Mitra, N. Goyal, Investigating packet dropping attacks in RPL-DODAG in IoT, [in:] 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, pp. 1–5, 2019, doi: 10.1109/I2CT45611.2019.9033926.
57. A. Verma, V. Ranga, ELNIDS: Ensemble learning based network intrusion detection system for RPL based Internet of Things, [in:] 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), Ghaziabad, India, pp. 1–6, 2019, doi: 10.1109/IoT-SIU.2019.8777504.
58. N.M. Müller, P. Debus, D. Kowatsch, K. Böttinger, Distributed anomaly detection of single mote attacks in RPL networks, [in:] Proceedings of the 16th International Joint Conference on e-Business and Telecommunications (ICETE), Prague, Czech Republic, Vol. 1, pp. 378–385, 2019, doi: 10.5220/0007836003780385.
59. A. Verma, V. Ranga, Evaluation of network intrusion detection systems for RPL based 6LoWPAN networks in IoT, Wireless Personal Communications, 108(3): 1571–1594, 2019, doi: 10.1007/S11277-019-06485-W.
60. Y. Al-Hadhrami, F.K. Hussain, A machine learning architecture towards detecting denial of service attack in IoT, [in:] Complex, Intelligent, and Software Intensive Systems (CISIS 2019), L. Barolli, F. Hussain, M. Ikeda [Eds.], Advances in Intelligent Systems and Computing, Vol. 993, pp. 417–429, Springer, Cham, 2019, doi: 10.1007/978-3-030-22354-0_37.
61. V. Neerugatti, A.R.M. Reddy, 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, P. Venkata Krishna, M. Obaidat [Eds.], Advances in Intelligent Systems and Computing, Vol. 1054, pp. 67–77, Springer, 2020, doi: 10.1007/978-981-15-0135-7_7.
62. S.O.M. Kamel, S.A. Elhamayed, Mitigating the impact of IoT routing attacks on power consumption in IoT healthcare environment using convolutional neural network, International Journal of Computer Network and Information Security (IJCNIS), 12(4): 11–29, 2020, doi: 10.5815/ijcnis.2020.04.02.
63. G. Thamilarasu, S. Chawla, Towards deep-learning-driven intrusion detection for the Internet of Things, Sensors, 19(9): 1977, 2019, doi: 10.3390/s19091977.
64. J. Foley, N. Moradpoor, H. Ochenyi, Employing a machine learning approach to detect combined Internet of Things attacks against two objective functions using a novel dataset, Security and Communication Networks, 2020(1): 2804291, 2020, doi: 10.1155/2020/2804291.
65. F. Medjek, D. Tandjaoui, N. Djedjig, I. Romdhani, Fault-tolerant AI-driven intrusion detection system for the Internet of Things, International Journal of Critical Infrastructure Protection, 34: 100436, 2021, doi: 10.1016/J.IJCIP.2021.100436.
66. R. Bokka, T. Sadasivam, Machine learning techniques to detect routing attacks in RPL based Internet of Things networks, International Journal of Electrical Engineering and Technology (IJEET), 12(6): 346–356, 2021, doi: 10.34218/IJEET.12.6.2021.033.
67. K.N. Qureshi, S.S. Rana, A. Ahmed, G. Jeon, A novel and secure attacks detection framework for smart cities industrial Internet of Things, Sustainable Cities and Society, 61: 102343, 2020, doi: 10.1016/J.SCS.2020.102343.