Abstract
Sensor networks have emerged as a promising technology for collecting data and capturing information about the physical world. However, these networks are often deployed in harsh and inaccessible environments, making them susceptible to various security attacks. One of the most severe attacks in wireless sensor networks (WSNs) is the Sybil attack, where a malicious node illegitimately assumes multiple fraudulent identities to deceive and disrupt the network. To address this complex and challenging problem, this paper proposes a dual trust-based multi-level Sybil (DTMS) attack detection approach for WSNs. The approach employs a multi-level detection system that verifies the identity and location of each node. At each level (Cluster Member, Cluster Head, and Base Station), a trust value is calculated based on the node's behavior. The trust value incorporates both communication trust and data trust, ensuring a satisfactory level of trust before accepting information from a node. The DTMS approach incorporates a dynamic reward and penalty coefficient in the trust function to accurately capture the severity of a node's behavior. Additionally, data aggregation techniques are employed to reduce communication overhead and conserve energy. The performance of DTMS is evaluated based on various metrics such as the severity of the trust function, true detection rate, false detection rate, residual energy, network lifetime, and packet loss ratio. Simulation results demonstrate that DTMS can effectively detect Sybil nodes, achieving a 100% detection rate in a malicious environment. Furthermore, a comparison with existing schemes highlights the desirable performance of DTMS across multiple parameters, including true detection rate, false detection rate, energy consumption, packet loss rate, and the number of active nodes.
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Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Nack, F. (2010). An overview on wireless sensor networks (Vol. 6). Institute of Computer Science (ICS), Freie Universität Berlin.
Jan, M. A., Nanda, P., He, X., & Liu, R. P. (2015). A sybil attack detection scheme for a centralized clustering-based hierarchical network. In 2015 IEEE Trustcom/BigDataSE/ISPA (Vol. 1, pp. 318–325). IEEE.
Jamshidi, M., Zangeneh, E., Esnaashari, M., Darwesh, A. M., & Meybodi, M. R. (2019). A novel model of sybil attack in cluster-based wireless sensor networks and propose a distributed algorithm to defend it. Wireless Personal Communications, 105, 145–173.
Angappan, A., Saravanabava, T. P., Sakthivel, P., & Vishvaksenan, K. S. (2020). Novel Sybil attack detection using RSSI and neighbour information to ensure secure communication in WSN. Journal of Ambient Intelligence and Humanized Computing, 6, 6567–6578.
Kumar, B., & Bhuyan, B. (2020). Game theoretical defense mechanism against reputation based sybil attacks. Procedia Computer Science, 167, 2465–2477.
Alsaedi, N., et al. (2017). Detecting sybil attacks in clustered wireless sensor networks based on energy trust system (ETS). Computer Communications, 110, 75–82.
Ishmanov, F., Kim, S., & Nam, S. (2015). A robust trust establishment scheme for wireless sensor networks. Sensors, 15(3), 7040–7061.
Ghai, S., Kumar, V., Kumar, R., & Vaid, R. (2021). Optimized multi-level data aggregation scheme (OMDA) for wireless sensor networks. In N. Marriwala, C. C. Tripathi, & D. Kumar (Eds.), Mobile radio communications and 5G networks (pp. 443–457). Singapore: Springer.
Li, M., Huiping, G., & Li, Q. (2021). Optimal number of cluster heads for selection cooperation in clustering wireless sensor networks. Journal of Physics: Conference Series, 1754(1), 012220.
Ishmanov, F., Kim, S. W., & Nam, S. Y. (2014). A secure trust establishment scheme for wireless sensor networks. Sensors, 14(1), 1877–1897.
Shaikh, R. A., et al. (2008). Group-based trust management scheme for clustered wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 20(11), 1698–1712.
Li, X., Zhou, F., & Junping, Du. (2013). LDTS: A lightweight and dependable trust system for clustered wireless sensor networks. IEEE Transactions on Information Forensics and Security, 8(6), 924–935.
Talbi, S., et al. (2017). Adaptive and dual data-communication trust scheme for clustered wireless sensor networks. Telecommunication Systems, 65(4), 605–619.
Singh, M., Sardar, A. R., Majumder, K., & Sarkar, S. K. (2017). A lightweight trust mechanism and overhead analysis for clustered WSN. IETE Journal of Research, 63(3), 297–308.
Liu, J., Xu, F. (2023). Research on trust-based secure routing in wireless sensor networks. In Third International conference on artificial intelligence and computer engineering (ICAICE 2022) vol. 12610, pp. 942–948. SPIE.
Zhang, T., Yan, L., & Yang, Y. (2016). Trust evaluation method for clustered wireless sensor networks based on cloud model. Wireless Networks, pp. 1–21
Khan, T., et al. (2019). A novel and comprehensive trust estimation clustering based approach for large scale wireless sensor networks. IEEE Access, 7(2019), 58221–58240.
Zhao, J., Huang, J., & Xiong, N. (2019). An effective exponential-based trust and reputation evaluation system in wireless sensor networks. IEEE Access, 7, 33859–33869.
Das, R., Dash, D., & Sarkar, M. K. (2020). HTMS: Fuzzy based hierarchical trust management scheme in WSN. Wireless Personal Communications pp. 1–34.
Fang, W., Zhang, W., Chen, W., Pan, T., Ni, Y., & Yang, Y. (2020) Trust-based attack and defense in wireless sensor networks: A survey. Wireless Communications and Mobile Computing.
Vaishnavi, S., & Sethukarasi, T. (2020). SybilWatch: a novel approach to detect Sybil attack in IoT based smart health care. Journal of Ambient Intelligence and Humanized Computing pp. 1–15
Quevedo, C. H. O. O., Quevedo, A. M. B. C., Campos, G. A., Gomes, R. L., Celestino, J., & Serhrouchni, A. (2020). An intelligent mechanism for sybil attacks detection in VANETs. In: ICC 2020–2020 IEEE international conference on communications (ICC) (pp. 1–6). IEEE, 2020. Paul, Aditi, Somnath
Faisal, S. M., & Zaidi, T. (2020). Timestamp based detection of sybil attack in VANET. IJ Network Security, 22(3), 397–408.
Paul, A., Sinha, S., & Pal, S. (2013) An efficient method to detect sybil attack using trust-based model. In Proceedings of international conference on advances in computer science, AETACS. Elsevier
Arifeen, M. M., Al Mamun, A., Ahmed, T., Kaiser, M. S., & Mahmud, M. (2021). A blockchain-based scheme for sybil attack detection in underwater wireless sensor networks. In Proceedings of international conference on trends in computational and cognitive engineering (pp. 467–476). Singapore: Springer
Khan, T., & Singh, K. (2021). TASRP: A trust aware secure routing protocol for wireless sensor networks. International Journal of Innovative Computing and Applications, 12(2–3), 108–122.
Das, R., & Dwivedi, M. (2023) Cluster head selection and malicious node detection using large-scale energy-aware trust optimization algorithm for HWSN. Journal of Reliable Intelligent Environments pp. 1–17
Lai, Y., Tong, L., Liu, J., Wang, Y., Tang, T., Zhao, Z., & Qin, H. (2022). Identifying malicious nodes in wireless sensor networks based on correlation detection. Computers & Security, 113, 102540.
Rani, S., Kumar, D., Singh, V. (2022). A trust-based mechanism to improve security of wireless sensor networks. In Proceedings of the international conference on intelligent vision and computing (ICIVC 2021) (pp. 36–54). Cham: Springer International Publishing
Kaur, M., Farid, F. (2023). A taxonomy of secure data transmission techniques: preventing sybil attacks in vehicular ad hoc networks. In Proceedings of the 2023 international conference on advances in computing research (ACR’23) (pp. 283–293). Cham: Springer Nature Switzerland
Tyagi, H., Kumar, R., & Pandey, S. K. (2023). A detailed study on trust management techniques for security and privacy in IoT: Challenges, trends, and research directions. High-Confidence Computing 100127.
Arshad, D., Asim, M., Tariq, N., Baker, T., Tawfik, H., & Al-Jumeily, O. B. E. (2022). THC-RPL: A lightweight Trust-enabled routing in RPL-based IoT networks against Sybil attack. PLoS ONE, 17(7), 10271277.
Almesaeed, R., & Al-Salem, E. (2022). Sybil attack detection scheme based on channel profile and power regulations in wireless sensor networks. Wireless Networks, 28(4), 1361–1374.
Jeyasekar, A., Antony Sheela, S., & Ansulin Jerusha, J. (2022). Outlier-based sybil attack detection in WSN. In IoT Based control networks and intelligent systems: proceedings of 3rd ICICNIS 2022 (pp. 497–517). Singapore: Springer Nature Singapore
Jane Nithya, K., Shyamala, K. (2022). A systematic review on various attack detection methods for wireless sensor networks. In: International conference on innovative computing and communications: Proceedings of ICICC 2021 (Vol. 3, pp. 183–204). Singapore: Springer.
Khan, T., & Singh, K. (2023). RTM: Realistic weight-based reliable trust model for large scale WSNs. Wireless Personal Communications, 129, 953–991.
Khan, T., Singh, K., Ahmad, K., & Ahmad, K. A. B. (2023). A secure and dependable trust assessment (SDTS) scheme for industrial communication networks. Scientific Reports, 13(1), 1910.
Shariq, M., Singh, K., Lal, C., Conti, M., & Khan, T. (2022). ESRAS: An efficient and secure ultra-lightweight RFID authentication scheme for low-cost tags. Computer Networks, 217, 109360.
Acknowledgements
This work was carried out in Secure and Computing laboratory, SC&SS, JNU, New Delhi, India and sponsored by the project entitled “Development of Intelligent Device for Security Enhancement (iEYE)” with sanction order: DST/TDT/DDP12/2017-G.
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The authors played significant roles in this research and paper. The first author primarily contributed by writing the original draft and developing the methodology. The second author contributed to conceptualization, supervision, formal analysis, investigation, and editing.
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Khan, T., Singh, K. DTMS: A Dual Trust-Based Multi-level Sybil Attack Detection Approach in WSNs. Wireless Pers Commun 134, 1389–1420 (2024). https://doi.org/10.1007/s11277-024-10948-0
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DOI: https://doi.org/10.1007/s11277-024-10948-0