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Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

Fei Wang, Zhen Dong*

School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, 73000, China

* Corresponding Author: Zhen Dong. Email: email

Computers, Materials & Continua 2024, 79(2), 2315-2329.


Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models, SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model. The dataset is first preprocessed using solo thermal encoding and normalization functions. Then the spiral convolution-Long Short-Term Memory Network model is constructed, which consists of spiral convolution, a two-layer long short-term memory network, and a classifier. It is shown through experiments that the model is characterized by high accuracy, small model computation, and fast convergence speed relative to previous deep learning models. The model uses a new neural network to achieve fast and accurate network traffic intrusion detection. The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes, respectively.


Cite This Article

APA Style
Wang, F., Dong, Z. (2024). Fusion of spiral convolution-lstm for intrusion detection modeling. Computers, Materials & Continua, 79(2), 2315-2329.
Vancouver Style
Wang F, Dong Z. Fusion of spiral convolution-lstm for intrusion detection modeling. Comput Mater Contin. 2024;79(2):2315-2329
IEEE Style
F. Wang and Z. Dong, "Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling," Comput. Mater. Contin., vol. 79, no. 2, pp. 2315-2329. 2024.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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