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E-SDNN: encoder-stacked deep neural networks for DDOS attack detection

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Abstract

The increasing reliance on internet-based services has heightened the vulnerability of network infrastructure to cyberattacks, particularly distributed denial of service (DDoS) attacks. These attacks can cause severe disruptions and significant financial losses. Early detection of malicious traffic is crucial in effectively combating such threats. This paper presents an innovative approach called the Encoder-Stacked deep neural networks (E-SDNN) model, which leverages Stacked/bagged multi-layer perceptrons (MLP) for accurate DDoS attack detection. The proposed method employs an encoder to select pertinent features from a preprocessed dataset, enabling precise attack detection. Extensive experiments were conducted on benchmark cybersecurity datasets, namely CICDS2017 and CICDDoS2019, encompassing various DDoS attack scenarios. The experimental results demonstrate the superiority of the E-SDNN model compared to state-of-the-art methods. The proposed E-SDNN model achieved an impressive overall accuracy rate of 99.94% and 98.86% for CICDDS2017 and CICDDoS2019, respectively.

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Data availability

The CICDS 2017 and CICDDoS2019 datasets were obtained from the Canadian Institute for Cybersecurity repository (https://www.unb.ca/cic/datasets.html). The CICDS 2017 dataset details and principles are outlined in [44]. In addition, the code is available on request.

Notes

  1. DDoS 2019 | Datasets | Research | Canadian Institute for Cybersecurity | UNB.

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Acknowledgements

The authors extend the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number R-2024-984.

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Authors and Affiliations

  1. Research Groups in Intelligent Machines, National School of Engineers (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Emna Benmohamed

  2. Department of Cyber Security, College of Engineering and Information Technology, Onaizah Colleges, P. O. Box 5371, Onaizah, Kingdom of Saudi Arabia

    Emna Benmohamed

  3. Applied College, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Kingdom of Saudi Arabia

    Adel Thaljaoui & Mansor Alohali

  4. Preparatory Institute for Engineering Studies of Gafsa, Gafsa, Tunisia

    Adel Thaljaoui

  5. Department of Computer Sciences, Faculty of Sciences of Gafsa, University of Gafsa, Gafsa, Tunisia

    Salim Elkhediri

  6. Department of Information Technology, College of Computer, Qassim University, Buraydah, Kingdom of Saudi Arabia

    Salim Elkhediri & Suliman Aladhadh

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  1. Emna Benmohamed
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Correspondence to Adel Thaljaoui.

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Benmohamed, E., Thaljaoui, A., Elkhediri, S. et al. E-SDNN: encoder-stacked deep neural networks for DDOS attack detection. Neural Comput & Applic 36, 10431–10443 (2024). https://doi.org/10.1007/s00521-024-09622-0

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