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An Intrusion Detection System using Deep Cellular Learning Automata and Semantic Hierarchy for Enhancing RPL Protocol Security

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Abstract

The internet of things (IoT) is a collection of systems connected to an online network consisting of things. Routing Protocol for Low-Power and Lossy Networks (RPL) is a proactive routing protocol for wireless networks based on distance vectors. Several methods have been proposed for improving RPL protocol security, suffering from lack of accuracy, the authenticity of intrusion detection, and lack of scalability. Therefore, in this research, an intrusion detection system based on deep cellular learning automata and semantic hierarchy is developed to increase RPL protocol security. Semantic hierarchy is used to transform attack features into significant values, and deep cellular learning automata are employed to increase the security of the RPL protocol. Here five datasets related to attacks, including Darknet, “Version Number”, “NSL-KDD”, “Botnet”, and Distributed Denial of Service (DDoS), have been used. Comparing the proposed results on five datasets indicates that the proposed method outperforms its counterparts. Also, the proposed model has been tested on Blackhole, NID, and BoT-IoT datasets based on ANN and CNN's Deep Neural Network. The results of penetration detection accuracy of the proposed method on Blackhole datasets, NID, and BoT-IoT were 99.65%, 99.71%, and 93.75%, respectively, which improved by averages of 0.42% compared to ANN and 0.55% compared to CNN methods.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Mohammad Shirafkan, Ali Shahidinejad & Mostafa Ghobaei-Arani

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  1. Mohammad Shirafkan
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  2. Ali Shahidinejad
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  3. Mostafa Ghobaei-Arani
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Contributions

MS, AS, MG-A conducted this research. MS: methodology, software, validation, writing original draft. AS: conceptualization, supervision, writing review & editing, formal analysis, project administration. MG-A: investigation, resources, data curation, visualization.

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Correspondence to Ali Shahidinejad.

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Shirafkan, M., Shahidinejad, A. & Ghobaei-Arani, M. An Intrusion Detection System using Deep Cellular Learning Automata and Semantic Hierarchy for Enhancing RPL Protocol Security. Cluster Comput 26, 2443–2461 (2023). https://doi.org/10.1007/s10586-022-03820-y

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