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Research Article |

Performance and Security Impacts of Blackhole Attacks on RPL-based IoT Networks for IoMT Applications

Author(s): Shameer M.1*, Rutravigneshwaran P.2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 3

Publisher : FOREX Publication

Published : 30 September 2025

e-ISSN : 2347-470X

Page(s) : 602-608

DOI: https://doi.org/10.37391/IJEER.130326

Abstract

Blackhole attacks stance a substantial menace to Routing Protocol for Low-Power and Lossy Networks (RPL)- based Internet of Things (IoT) networks. This study investigates the impact of such attacks on key performance metrics, including power consumption and Directed Acyclic Graph Identification Object (DIO) packet delivery. Using the Contiki and Cooja simulators, we analyze the effects of scenario-based blackhole attacks under various conditions. Our findings show that blackhole attacks lead to significant increases in CPU and radio energy consumption, with CPU utilization rising by 15-20% and radio listen energy increasing by 20-25%, while idle power consumption remains unchanged. These results highlight network vulnerabilities and inform the design of more resilient, trust-centric IoT networks, particularly for critical applications like remote healthcare monitoring.

Keywords: RPL Attacks, IoT Security, Blackhole Attacks, IoMT, 6LoWPAN.




Shameer M., Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India; Email: mdsameer09@gmail.com

Rutravigneshwaran P., Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India; Email: rutra20190@gmail.com

    [1] S. S. Hameed et al., “A Hybrid Lightweight System for Early Attack Detection in the IoMT Fog,” Sensors, vol. 21, no. 24, p. 8289, Dec. 2021, doi: 10.3390/s21248289. Available: https://www.mdpi.com/1424-8220/21/24/8289. [Accessed: Dec. 31, 2024]
    [2] S.-E. Chafi, Y. Balboul, S. Mazer, M. Fattah, and M. El Bekkali, “Resource placement strategy optimization for smart grid application using 5G wireless networks,” IJECE, vol. 12, no. 4, p. 3932, Aug. 2022, doi: 10.11591/ijece.v12i4.pp3932-3942. Available: http://ijece.iaescore.com/index.php/IJECE/article/view/26814. [Accessed: Dec. 31, 2024]
    [3] F. Wahab et al., “An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–11, Aug. 2022, doi: 10.1155/2022/6096289. Available: https://www.hindawi.com/journals/cin/2022/6096289/. [Accessed: Mar. 19, 2025]
    [4] K. Vaisakhkrishnan, G. Ashok, P. Mishra, and T. G. Kumar, “Guarding Digital Health: Deep Learning for Attack Detection in Medical IoT,” Procedia Computer Science, vol. 235, pp. 2498–2507, 2024, doi: 10.1016/j.procs.2024.04.235. Available: https://linkinghub.elsevier.com/retrieve/pii/S1877050924009116. [Accessed: Dec. 26, 2024]
    [5] J. A. Shaikh et al., “A UAV-Assisted Stackelberg Game Model for Securing loMT Healthcare Networks,” Drones, vol. 7, no. 7, p. 415, Jun. 2023, doi: 10.3390/drones7070415. Available: https://www.mdpi.com/2504-446X/7/7/415. [Accessed: Dec. 26, 2024]
    [6] N. Alfriehat et al., “RPL-based attack detection approaches in IoT networks: review and taxonomy,” Artif Intell Rev, vol. 57, no. 9, p. 248, Aug. 2024, doi: 10.1007/s10462-024-10907-y. Available: https://link.springer.com/10.1007/s10462-024-10907-y. [Accessed: Dec. 25, 2024]
    [7] T. A. Al-Amiedy, M. Anbar, and B. Belaton, “OPSMOTE-ML: an optimized SMOTE with machine learning models for selective forwarding attack detection in low power and lossy networks of internet of things,” Cluster Comput, vol. 27, no. 9, pp. 12141–12184, Dec. 2024, doi: 10.1007/s10586-024-04598-x. Available: https://link.springer.com/10.1007/s10586-024-04598-x. [Accessed: Dec. 26, 2024]
    [8] A. T. Mathew and P. Mani, “Strength of Deep Learning-based Solutions to Secure Healthcare IoT: A Critical Review,” TOBEJ, vol. 17, no. 1, p. e187412072304060, May 2023, doi: 10.2174/18741207-v17-e230505-2022-HT28-4371-2. Available: https://openbiomedicalengineeringjournal.com/VOLUME/17/ELOCATOR/e187412072304060/. [Accessed: Dec. 26, 2024]
    [9] D. A. Chaudhari and E. Umamaheswari, “A new adaptive XOR, hashing and encryption-based authentication protocol for secure transmission of the medical data in Internet of Things (IoT),” Biomedical Engineering / Biomedizinische Technik, vol. 66, no. 1, pp. 91–105, Feb. 2021, doi: 10.1515/bmt-2019-0123. Available: https://www.degruyter.com/document/doi/10.1515/bmt-2019-0123/html. [Accessed: Dec. 26, 2024]
    [10] J. Sun, F. Khan, J. Li, M. D. Alshehri, R. Alturki, and M. Wedyan, “Mutual Authentication Scheme for the Device-to-Server Communication in the Internet of Medical Things,” IEEE Internet Things J., vol. 8, no. 21, pp. 15663–15671, Nov. 2021, doi: 10.1109/JIOT.2021.3078702. Available: https://ieeexplore.ieee.org/document/9426898/. [Accessed: Nov. 16, 2024]
    [11] T. Winter et al., “RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks,” RFC Editor, RFC6550, Mar. 2012. doi: 10.17487/rfc6550. Available: https://www.rfc-editor.org/info/rfc6550. [Accessed: Dec. 27, 2024]
    [12] Anthea Mayzaud, Remi Badonnel, and Isabelle Chrisment, “A Taxonomy of Attacks in RPL-based Internet of Things,” International Journal of Network Security, vol. 18, no. 3, May 2016, doi: 10.6633/IJNS.201605.18(3).07
    [13] N. Panda and M. Supriya, “Blackhole Attack Prediction in Wireless Sensor Networks Using Support Vector Machine,” in Advances in Signal Processing, Embedded Systems and IoT, V. V. S. S. S. Chakravarthy, V. Bhateja, W. Flores Fuentes, J. Anguera, and K. P. Vasavi, Eds., in Lecture Notes in Electrical Engineering, vol. 992. Singapore: Springer Nature Singapore, 2023, pp. 321–331. doi: 10.1007/978-981-19-8865-3_30. Available: https://link.springer.com/10.1007/978-981-19-8865-3_30. [Accessed: Dec. 29, 2024]
    [14] S. M. and G. L., “K-Means Clustering-Based Trust (KmeansT) Evaluation Mechanism for Detecting Blackhole Attacks in IoT Environment,” IJCDS, vol. 15, no. 1, pp. 739–751, Aug. 2024, doi: 10.12785/ijcds/160154. Available: https://journals.uob.edu.bh/handle/123456789/5289. [Accessed: Dec. 21, 2024]
    [15] P. Musikawan, Y. Kongsorot, P. Aimtongkham, and C. So-In, “Enhanced Multigrained Scanning-Based Deep Stacking Network for Intrusion Detection in IoMT Networks,” IEEE Access, vol. 12, pp. 152482–152497, 2024, doi: 10.1109/ACCESS.2024.3480011. Available: https://ieeexplore.ieee.org/document/10716376/. [Accessed: Dec. 26, 2024]
    [16] A. Shaji and N. S. Nair, “A Novel Trust Based Two Phase Algorithm to Detect Sybil Attack in IoMT Networks,” in 2023 9th International Conference on Smart Computing and Communications (ICSCC), Kochi, Kerala, India: IEEE, Aug. 2023, pp. 309–314. doi: 10.1109/ICSCC59169.2023.10334946. Available: https://ieeexplore.ieee.org/document/10334946/. [Accessed: Dec. 26, 2024]
    [17] M. Javed, N. Tariq, M. Ashraf, F. A. Khan, M. Asim, and M. Imran, “Securing Smart Healthcare Cyber-Physical Systems against Blackhole and Greyhole Attacks Using a Blockchain-Enabled Gini Index Framework,” Sensors, vol. 23, no. 23, p. 9372, Nov. 2023, doi: 10.3390/s23239372. Available: https://www.mdpi.com/1424-8220/23/23/9372. [Accessed: Dec. 26, 2024]
    [18] S. S. Hameed, W. H. Hassan, L. Abdul Latiff, and F. Ghabban, “A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches,” PeerJ Computer Science, vol. 7, p. e414, Mar. 2021, doi: 10.7717/peerj-cs.414. Available: https://peerj.com/articles/cs-414. [Accessed: Dec. 26, 2024]
    [19] S. Abbas, G. A. Sampedro, M. Abisado, A. Almadhor, I. Yousaf, and S.-P. Hong, “Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things,” Electronics, vol. 12, no. 12, p. 2612, Jun. 2023, doi: 10.3390/electronics12122612. Available: https://www.mdpi.com/2079-9292/12/12/2612. [Accessed: Dec. 26, 2024]
    [20] A. S. Ahmed and H. A. Salah, “Development a Software Defined Network (SDN) with Internet of Things (IoT) Security for Medical Issues,” JQCM, vol. 15, no. 3, Sep. 2023, doi: 10.29304/jqcm.2023.15.3.1268. Available: https://jqcsm.qu.edu.iq/index.php/journalcm/article/view/1268. [Accessed: Dec. 27, 2024]
    [21] N. S., S. Palanisamy, and N. T., “Achieving Secured Medical Network (SMN) through Stateless Mechanism and SkeyM in Medical-Internet of Things (M-IoT),” J. Eng. Appl. Sci., vol. 71, no. 1, p. 128, Dec. 2024, doi: 10.1186/s44147-024-00460-4. Available: https://jeas.springeropen.com/articles/10.1186/s44147-024-00460-4. [Accessed: Dec. 27, 2024]
    [22] S. Prajapat, P. Kumar, D. Kumar, A. Kumar Das, M. Shamim Hossain, and J. J. P. C. Rodrigues, “Quantum Secure Authentication Scheme for Internet of Medical Things Using Blockchain,” IEEE Internet Things J., vol. 11, no. 23, pp. 38496–38507, Dec. 2024, doi: 10.1109/JIOT.2024.3448212. Available: https://ieeexplore.ieee.org/document/10643610/. [Accessed: Dec. 27, 2024]
    [23] Y. Rbah et al., “Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity,” IJ-AI, vol. 13, no. 3, p. 3599, Sep. 2024, doi: 10.11591/ijai.v13.i3.pp3599-3610. Available: https://ijai.iaescore.com/index.php/IJAI/article/view/24892. [Accessed: Dec. 27, 2024]

Shameer M., Rutravigneshwaran P. (2025), Performance and Security Impacts of Blackhole Attacks on RPL-based IoT Networks for IoMT Applications. IJEER 13(3), 602-608. DOI: 10.37391/IJEER.130326.