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Deep learning-based distributed denial-of-service detection

Author

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  • Hanene Mennour
  • Sihem Mostefai

Abstract

The nuisance of distributed denial-of-service (DDoS) attacks has extended unremittingly nowadays. Thus, guaranteeing system availability in this open-ended pandemic is a crucial task. In this work, we propose three different deep learning strategies as a network anomaly-based intrusion detection system (N-IDS) for a DDoS multi-classification task. We built a deep convolutional neural network (CNN), a stacked long short-term memory (S-LSTM) neural network which is a distinct artificial recurrent neural network (RNN), the third model is a hybridisation between CNN and LSTM. Then, we evaluated them on three up to date flow-based datasets: CICIDS2017, CICDDoS2019 and BoT-IoT benchmarks. The outcomes demonstrate that hybrid CNN-LSTM outperforms the existing state-of-the-art schemes in almost all the validation metrics.

Suggested Citation

  • Hanene Mennour & Sihem Mostefai, 2022. "Deep learning-based distributed denial-of-service detection," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 26(1/2), pages 80-103.
    • Handle: RePEc:ids:ijnvor:v:26:y:2022:i:1/2:p:80-103
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