DDoS attack detection model based on CNN-LSTM

Haizhao Min; Zhibo Yang; Haipeng Sun; Pengfei Zhang

doi:10.1117/12.3012305

21 December 2023 DDoS attack detection model based on CNN-LSTM

Haizhao Min, Zhibo Yang, Haipeng Sun, Pengfei Zhang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297048 (2023) https://doi.org/10.1117/12.3012305
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

DDoS attacks exhaust computer resources, such as computational power and network connectivity, leading to resource depletion and hindering the service availability for legitimate users or providing degraded services. In this paper, we propose a machine learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for effective DDoS attack detection. We validate the performance of our model on the CIC-IDS2017 dataset, achieving accuracies of 99.67% and 99.55% on the training and testing sets, respectively. In addition, we also computed metrics such as Recall, False Discovery Rate (FDR), False Negative Rate (FNR), F1-Score, etc., which all serve as indicators reflecting the high effectiveness of our model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haizhao Min, Zhibo Yang, Haipeng Sun, and Pengfei Zhang "DDoS attack detection model based on CNN-LSTM", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297048 (21 December 2023); https://doi.org/10.1117/12.3012305

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