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Belief-DDoS: stepping up DDoS attack detection model using DBN algorithm

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Abstract

Distributed Denial of Services (DDoS) attacks severely impact various systems. Traditional approaches like signature-based and scrubbing methods remain shortcomings in detecting extensive sophisticated attacks. Thus, this paper proposes a Deep Belief Network (DBN) to construct an intelligent detection model using automated feature representation. Instead of using conventional machine learning methods, we employ the DBN to train a classification model that can effectively detect DDoS attacks. Based on the experimental results, our proposed model can obtain a higher accuracy with a tiny loss.

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Data supporting this study are openly available upon reasonable request.

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Acknowledgements

This paper is conducted in the Department of Informatics, Universitas Respati Yogyakarta, Indonesia.

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  1. Department of Informatics, Universitas Respati Yogyakarta, Yogyakarta, Indonesia

    Putra Wanda & Marselina Endah Hiswati

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  1. Putra Wanda
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  2. Marselina Endah Hiswati
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Correspondence to Putra Wanda.

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Wanda, P., Hiswati, M.E. Belief-DDoS: stepping up DDoS attack detection model using DBN algorithm. Int. j. inf. tecnol. 16, 271–278 (2024). https://doi.org/10.1007/s41870-023-01631-x

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