@Article{cmc.2024.048443,
AUTHOR = {Fei Wang, Zhen Dong},
TITLE = {Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {79},
YEAR = {2024},
NUMBER = {2},
PAGES = {2315--2329},
URL = {http://www.techscience.com/cmc/v79n2/56417},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2024.048443}
}