Deep-learning-based intrusion detection methods

Hong Chen; Lili Chen

doi:10.1117/12.2683212

26 June 2023 Deep-learning-based intrusion detection methods

Hong Chen, Lili Chen

Proceedings Volume 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023); 1271403 (2023) https://doi.org/10.1117/12.2683212
Event: Third International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 2023, Sanya, China

Abstract

With the increasing number and categories of cyber-attacks in the era of big data, intrusion detection techniques are constantly updated and optimized. In order to address the shortcomings of traditional intrusion detection methods and single neural network models in intrusion detection, this paper proposes a deep learning-based intrusion detection method. By combining a one-dimensional convolutional neural network with a bidirectional gated recurrent unit, a new CNN-BiGRU network model is formed to fully extract intrusion detection data features and improve the accuracy of intrusion detection. Using the public dataset CIC-IDS2017, multiple sets of comparison experiments were conducted on the proposed method in this paper in the same environment. Firstly, the ablation experiments yielded that in terms of detection performance, the accuracy of this paper's method improved from 99.50% and 99.34% to 99.58% compared with CNN and BiGRU-based models respectively. In terms of running time, the detection time of this model is reduced from 1921s to 907s compared with that of BiGRU, demonstrating the good detection performance of this method. Finally, through comparison tests, it is verified that the intrusion detection method proposed in this paper has better detection performance compared with other methods.

Citation Download Citation

Hong Chen and Lili Chen "Deep-learning-based intrusion detection methods", Proc. SPIE 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 1271403 (26 June 2023); https://doi.org/10.1117/12.2683212

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KEYWORDS

Computer intrusion detection

Deep learning

Machine learning

Convolution

Convolutional neural networks

Ablation

Neural networks

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