Enhancing IoT-LLN Security with IbiboRPLChain Solution: A Blockchain-Based Authentication Method

  • Ibibo J
  • Balota J
  • Alwada'n T
  • et al.
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Abstract

The security of Internet of Things (IoT)–Low-Power and Lossy Networks (LLNs) is crucial for their widespread adoption in various applications. The standard routing protocol for IoT-LLNs, IPv6 Routing Protocol over Low-Power and Lossy Networks (RPL), is susceptible to insider attacks, such as the version number attack (VNA), decreased rank attack (DRA), and increased rank attack (IRA). These attacks can significantly impact network performance and resource consumption. To address these security concerns, we propose the IbiboRPLChain Solution, a secure blockchain-based authentication method for RPL nodes. The proposed solution introduces an additional blockchain layer to the RPL architecture, enabling secure authentication of communication links between the routing layer and the sensor layer. The IbiboRPLChain Solution utilises smart contracts to trigger immediate authentication upon detecting routing attacks initiated by malicious nodes in an IoT-LLN environment. The evaluation of the proposed solution demonstrates its superior performance in mitigating insider attacks and enhancing IoT-LLN security compared to existing methods. The proposed solution effectively mitigates insider attacks by employing blockchain technology to authenticate communication links between routing and sensor nodes. This prevents malicious nodes from manipulating routing information and disrupting network operations. Additionally, the solution enhances IoT-LLN security by utilising smart contracts to trigger immediate authentication upon detecting suspicious activity, ensuring prompt action against potential threats. The findings of this research have significant implications for the development and deployment of secure IoT-LLNs.

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APA

Ibibo, J. T., Balota, J. E., Alwada’n, T., & Akinade, O. O. (2025). Enhancing IoT-LLN Security with IbiboRPLChain Solution: A Blockchain-Based Authentication Method. Applied Sciences, 15(19), 10557. https://doi.org/10.3390/app151910557

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