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lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning

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Abstract

Smart cities are being enabled all around the world by Internet of Things (IoT) applications. A smart city idea necessitates the integration of information and communication technologies and devices throughout a network in order to provide improved services to consumers. Because of their increasing amount and mobility, they are increasingly appealing to attackers. Therefore, several solutions, including as encryptions, authentication, availability, and data integrity, have been combined to protect IoT. Intrusion detection systems (IDSs) are a powerful security tool that may be improved by incorporating machine learning (ML) and deep learning (DP) techniques. This paper presents a novel intrusion detection framework for IoT-based smart environments with Ensemble Learning called IDS-SIoEL. Typically, the framework proposed an optimal anomaly detection model that uses AdaBoost, and combining different feature selection techniques Boruta, mutual information and correlation furthermore. The proposed model was evaluated on IoT-23, BoT-IoT, and Edge-IIoT datasets using the GPU. When compared to existing IDS, our approach provides good rating performance features of ACC, recall, and precision, with around 99.9% on record detection and calculation time of 33.68 s for learning and 0.02156 s for detection.

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Data availability

Assessments and experimental results, obtained using Anaconda 3 IDE, are available and will be shared with authors at https://sites-Google.com/umi.ac.ma/azrour.

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Funding

This study was not funded and without financially supporting. We did this research work as professors of computer sciences at University.

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Authors and Affiliations

  1. Technology Higher School Essaouira, Cadi Ayyad University, Marrakesh, Morocco

    Chaimae Hazman, Azidine Guezzaz & Said Benkirane

  2. IDMS Team, Faculty of Sciences and Technics Errachidia, Moulay Ismail University of Meknès, Meknès, Morocco

    Mourade Azrour

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  1. Chaimae Hazman
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  2. Azidine Guezzaz
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Contributions

CH is the main author that manages the contribution and gives the detailed description of the model. AG writes the abstract, introduction and analyzes the related works section. SB evaluates the results obtained from implementation and drowing the figures. MA participates in implementation of the model prepared the final manuscript and corrected the English language.

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Correspondence to Azidine Guezzaz.

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Hazman, C., Guezzaz, A., Benkirane, S. et al. lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning. Cluster Comput 26, 4069–4083 (2023). https://doi.org/10.1007/s10586-022-03810-0

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