• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 44, Pages: 2363-2374

Original Article

Secured Technique to Detect and Avoid Malicious Nodes in Internet of Things

Received Date:04 July 2022, Accepted Date:16 October 2022, Published Date:30 November 2022


Objectives: To detect rank attacks during topology establishment and updated the RPL Destination Oriented Directed Acyclic Graph (DODAG) formation algorithm. The algorithm’s distributed module runs across all participating nodes, while the centralized module runs in the sink. Methods: The integrity and authenticity of control messages transmitted among two nodes and the sink are verified using a lightweight Hashed Message Authentication Code - Light-weight One-way Cryptographic Hash Algorithm (HMAC-LOCHA). The Secured Technique to Detect and Avoid Malicious Nodes (STDAMN) technique is proposed to overcome the rank attack of the nodes. Findings: The proposed scheme STDAMN outperforms the LEADER and SBIDS schemes when considering 50% malicious nodes, the accuracy rate of STDAMN is 3% higher than LEADER and 17% higher than SBIDS in Security mode whereas it is 4% and 14% higher in non-security mode respectively in the decreased rank attack. Again considering 50% malicious nodes, the accuracy rate of STDAMN is 2% higher than LEADER and 13% higher than SBIDS in with- Security mode whereas it is 2.2% and 16.1% higher in without-security mode respectively in the increased rank attack. Also indeed, the false positive rate for STDAMN is lower by 72.5% and 72%, 15.3% and 21.2% whereas the false negative rate for STDAMN is lower by 77.2% and 62.1%, 32.5%, and 39.5% on average for with-security and without-security respectively than LEADER and SBIDS in the decreased rank attack. Novelty: This paper presents a rank attack detection approach for non-storing mode RPL used in IoT to cope with both increased and decreased rank attacks to address this issue. The performance of the suggested technique is assessed both conceptually and through simulation using the Contiki-based Cooja simulator. The proposed technique surpasses state-of-the-art rank attack detection techniques in terms of detection accuracy and false positive/negative rate while maintaining acceptable network performance, according to simulation results.

Keywords: Security; RPL; Rank attack; HMAC; Malicious Node; DODAG


  1. Karmakar S, Sengupta J, Bit SD. LEADER: Low Overhead Rank Attack Detection for Securing RPL based IoT. International Conference on COMmunication Systems & NETworkS (COMSNETS). 2021. Available from: https://doi:10.1109/COMSNETS51098.2021.9352937
  2. Verma A, Ranga V. CoSec-RPL: detection of copycat attacks in RPL based 6LoWPANs using outlier analysis. Telecommunication Systems. 2020;75(1):43–61. Available from: https://doi.org/10.1007/s11235-020-00674-w
  3. Mahyoub M, Mahmoud ASH, Abu-Amara M, Sheltami TR. An Efficient RPL-Based Mechanism for Node-to-Node Communications in IoT. IEEE Internet of Things Journal. 2021;8(9):7152–7169. Available from: https://doi:10.1109/JIOT.2020.3038696
  4. Pasikhani AM, Clark JA, Gope P, Alshahrani A. Intrusion Detection Systems in RPL-Based 6LoWPAN: A Systematic Literature Review. IEEE Sensors Journal. 2021;21(11):12940–12968. Available from: https://doi:10.1109/JSEN.2021.3068240
  5. Boudouaia MA, Abouaissa A, Benayache A, Lorenz P. Divide and Conquer-based Attack against RPL Routing Protocol. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. 2020. Available from: https://doi:10.1109/GLOBECOM42002.2020.9322275
  6. Wadhaj I, Ghaleb B, Thomson C, Al-Dubai A, Buchanan WJ. Mitigation Mechanisms Against the DAO Attack on the Routing Protocol for Low Power and Lossy Networks (RPL) IEEE Access. 2020;8:43665–43675. Available from: https://doi:10.1109/ACCESS.2020.2977476
  7. Agiollo A, Conti M, Kaliyar P, Lin TN, Pajola L. DETONAR: Detection of Routing Attacks in RPL-Based IoT. IEEE Transactions on Network and Service Management. 2021;18(2):1178–1190. Available from: https://doi:10.1109/TNSM.2021.3075496
  8. Almusaylim ZA, Alhumam A, Jhanjhi NZ. Proposing a Secure RPL based Internet of Things Routing Protocol: A Review. Ad Hoc Networks. 2020;101:102096. Available from: https://doi.org/10.1016/j.adhoc.2020.102096
  9. Avila K, Jabba D, Gomez J. Security Aspects for Rpl-Based Protocols: A Systematic Review in IoT. Applied Sciences. 2020;10(18):6472. Available from: https://doi.org/10.3390/app10186472
  10. Boudouaia MA, Ali-Pacha A, Abouaissa A, Lorenz P. Security Against Rank Attack in RPL Protocol. IEEE Network. 2020;34(4):133–139. Available from: https://doi:10.1109/MNET.011.1900651
  11. Mangelkar S, Dhage SN, Nimkar AV. A comparative study on RPL attacks and security solutions. International Conference on Intelligent Computing and Control (I2C2). 2017. Available from: https://doi:10.1109/I2C2.2017.8321851
  12. Muzammal SM, Murugesan RK, Jhanjhi NZ, Jung LT. SMTrust: Proposing Trust-Based Secure Routing Protocol for RPL Attacks for IoT Applications. International Conference on Computational Intelligence (ICCI). 2020. Available from: https://doi:10.1109/ICCI51257.2020.9247818
  13. Arena A, Perazzo P, Vallati C, Dini G, Anastasi G. Evaluating and improving the scalability of RPL security in the Internet of Things. Computer Communications. 2020;151:119–132. Available from: https://doi.org/10.1016/j.comcom.2019.12.062
  14. Ganesh DR, Patil KK, Suresh L. A Multicast Transmission Routing Protocol for Low Power Lossy Network Based IoT Ecosystem. Intelligent Data Communication Technologies and Internet of Things. 2019;p. 569–582. Available from: https://doi.org/10.1007/978-3-030-34080-3_65
  15. Lai Y, Tong L, Liu J, Wang Y, Tang T, Zhao Z, et al. Identifying malicious nodes in wireless sensor networks based on correlation detection. Computers & Security. 2022;113:102540. Available from: https://doi.org/10.1016/j.cose.2021.102540
  16. Simoglou G, Violettas G, Petridou S, Mamatas L. Intrusion detection systems for RPL security: A comparative analysis. Computers & Security. 2021;104:102219. Available from: https://doi.org/10.1016/j.cose.2021.102219
  17. Malik K, Rehman F, Maqsood T, Mustafa S, Khalid O, Akhunzada A. Lightweight Internet of Things Botnet Detection Using One-Class Classification. Sensors. 22(10):3646. Available from: https://doi.org/10.3390/s22103646
  18. Vaishnavi S, Sethukarasi T. Detection and Avoidance of Clone Attack in IoT Based Smart Health Application. Intelligent Automation & Soft Computing. 2022;31(3):1919–1937. Available from: https://doi.org/10.32604/iasc.2022.021006
  19. Bint M, Sajid E, Ullah S, Javaid N, Ullah I, Qamar AM, et al. Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain. Wireless Communications and Mobile Computing. 2022. 7386049.1- 16. 2022. Available from: https://doi.org/10.1155/2022/7386049
  20. Prathapchandran K, Janani T. A Trust-Based Security Model to Detect Misbehaving Nodes in Internet of Things (IoT) Environment using Logistic Regression. Journal of Physics: Conference Series. 2021;1850(1):012031. Available from: https://doi:10.1088/1742-6596/1850/1/012031


© 2022 Newton & Samuel. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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