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A New Secure Model for Cloud Environments Using RBFNN and AdaBoost

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Abstract

The emergence of data today requires the enhancement of cybersecurity due to the importance and sensitivity of the information contained in data. Therefore, Cloud Computing is a new model that has made it convenient to distribute and store data because of its distinctive characteristics. Security, though, is a major worry for cloud services due to the various risks they confront regularly, which increases the danger of losing consumer trust. In this regard, many tools have been used to secure Cloud. Recently, Intrusion Detection System (IDS) is a frequently used security tool because of its effectiveness. But when confronted with an important volume and variety of network data, the current IDS might show overfitting, poor classification accuracy (ACC) and high rates of false positives (FPR). Then to develop IDS, studies used new technologies like Machine Learning, Deep Learning and Ensemble Learning. In this article to mitigate the above problems the used datasets are preprocessed with Min–Max normalization and dummies function. Furthermore, this model is built on the Radial Basis Function Neural Network (RBFNN) and AdaBoost approaches for anomaly detection. In particular, the RBFNN classifier has been added to boost detection precision and support effective decision-making, while the AdaBoost is employed to improve feature selection and training. In addition, the principal idea is to mention the impact of integrating feature selection. To demonstrate this, we give a comparison between the two models. In the first one, we directly apply RBFNN after preprocessing data, in the second one, we integer AdaBoost for feature selection, then we apply RBFNN for prediction. Finally, the NSL-KDD and CICIDS2017 datasets are used in the trials. Our suggested idea achieves 99.7% and 99.5% ACC, 0.6% and 0.18% FPR, respectively. The results produced using AdaBoost for feature selection have demonstrated that our suggested concept is more effective than utilizing RBFNN directly also it performs well compared with the other studies.

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Funding

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

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Authors and Affiliations

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

    Hanaa Attou, Azidine Guezzaz & Said Benkirane

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

    Mourade Azrour

Authors
  1. Hanaa Attou
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  2. Azidine Guezzaz
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  3. Said Benkirane
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  4. Mourade Azrour
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Contributions

Hanaa Attouis the main author that manages the contribution and gives the detailed description of the model. AzidineGuezzaz writes the abstract, introduction and analyzes the related works section. Said Benkirane evaluates the results obtained from implementation and drowing the figures. MouradeAzrour participates in implementation of the model prepared the final manuscript and corrected the English language.

Corresponding author

Correspondence to Azidine Guezzaz.

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Attou, H., Guezzaz, A., Benkirane, S. et al. A New Secure Model for Cloud Environments Using RBFNN and AdaBoost. SN COMPUT. SCI. 6, 188 (2025). https://doi.org/10.1007/s42979-025-03691-1

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