A Trust and Energy-based routing framework for the IoT network

Document Type : Original Article

Authors

1 Computer faculty - K. N. Toosi University of technology

2 Computer Engineering, K. N. Toosi University of Technology

Abstract

Today, when a connection is established between people's lives and the application space based on the Internet of Things, it is necessary to make the platform of this new technology more secure and reliable. The applications of IoT have an urgent need for security issues such as trust, and many attacks can easily target the sensor nodes. To achieve this goal, we chose the RPL protocol due to its wide application and weak security. We investigated it using an innovative method of penetration testing. In this research, an application-based selective forwarding attack has been implemented. In the literature, criteria such as the number of sent packets, the amount of remaining energy, and the packet discard rate were used to detect the attacker but in this solution, the signal strength indicator is used to detect the attacker, and the parameters of positive and negative behaviour are used to calculate the trust in the beta function. In this study, trust is calculated based on the application, and the attacking node reduces the signal strength instead of increasing it.The simulation results show that the proposed method has a 99% attack detection rate, less than 11% FNR while improving the packet delivery rate.

Keywords

Main Subjects

References

[1]  Almusaylim, Z. A., Alhumam, A., & Jhanjhi, N. Z. (2020). Proposing a secure RPL based internet of things routing protocol: a review. Ad Hoc Networks, 101, 102096.
[2]  Ioulianou, P. P., Vassilakis, V. G., & Shahandashti, S. F. (2022). 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.‏
[3]  Ribera, E. G., Alvarez, B. M., Samuel, C., Ioulianou, P. P., & Vassilakis, V. G. (2020, July). Heartbeat-based detection of blackhole and greyhole attacks in RPL networks. In 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) (pp. 1-6). IEEE.‏
[4] Jain, A., & Jain, S. (2019). A survey on miscellaneous attacks and countermeasures for RPL routing protocol in IoT. In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2018, Volume 3 (pp. 611-620). Springer Singapore.‏
[5] Wang, R., Zhang, Z., Zhang, Z., & Jia, Z. (2018). ETMRM: An energy-efficient trust management and routing mechanism for SDWSNs. Computer Networks, 139, 119-135.‏
[6]  Nie, S. (2019). A novel trust model of dynamic optimization based on entropy method in wireless sensor networks. Cluster Computing, 22(Suppl 5), 11153-11162.‏
[8]  Patel, A., & Jinwala, D. (2022). A reputation‐based RPL protocol to detect selective forwarding attack in Internet of Things. International Journal of Communication Systems, 35(1), e5007.‏
[9]  Zhang, Q., & Zhang, W. (2019). Accurate detection of selective forwarding attack in wireless sensor networks. International Journal of Distributed Sensor Networks, 15(1), 1550147718824008.
[10]  Ioannou, C., & Vassiliou, V. (2019, May). Classifying security attacks in IoT networks using supervised learning. In 2019 15th International conference on distributed computing in sensor systems (DCOSS) (pp. 652-658). IEEE.‏ ‏
[11] Yu, B., & Xiao, B. (2006, April). Detecting selective forwarding attacks in wireless sensor networks. In Proceedings 20th IEEE international parallel & distributed processing symposium (pp. 8-pp). IEEE.‏
[12]  Udhayavani, M., & Chandrasekaran, M. (2019). Design of TAREEN (trust aware routing with energy efficient network) and enactment of TARF: A trust-aware routing framework for wireless sensor networks. Cluster Computing, 22(Suppl 5), 11919-11927.‏
 [13]  Raza, S., Wallgren, L., & Voigt, T. (2013). SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks, 11(8), 2661-2674.‏
[14]  Santos, A. L., Cervantes, C. A., Nogueira, M., & Kantarci, B. (2019). Clustering and reliability-driven mitigation of routing attacks in massive IoT systems. Journal of Internet Services and Applications, 10, 1-17.‏
[15]  Airehrour, D., Gutierrez, J. A., & Ray, S. K. (2019). SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things. Future Generation Computer Systems, 93, 860-876.
[16]  Neerugatti, V., & Rama Mohan Reddy, A. (2020). 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: Proceedings of CCODE 2019 (pp. 67-77). Springer Singapore.‏
[17] Yarinezhad, R., & Sarabi, A. (2018). Reducing delay and energy consumption in wireless sensor networks by making virtual grid infrastructure and using mobile sink. AEU-International Journal of Electronics and Communications, 84, 144-152.‏
[18] Yarinezhad, R. (2019). Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure. Ad Hoc Networks, 84, 42-55.
[19] ul Hassan, T., Asim, M., Baker, T., Hassan, J., & Tariq, N. (2021). CTrust‐RPL: A control layer‐based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks‐based Internet of Things applications. Transactions on Emerging Telecommunications Technologies, 32(3), e4224.‏
[20]  Fang, W., Zhang, C., Shi, Z., Zhao, Q., & Shan, L. (2016). BTRES: Beta-based trust and reputation evaluation system for wireless sensor networks. Journal of Network and Computer Applications, 59, 88-94.‏
[21]  Bauer, J., & Aschenbruck, N. (2020). Towards a low-cost rssi-based crop monitoring. ACM Transactions on Internet of Things, 1(4), 1-26.‏

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