TY  - EJOU
AU  - Wang, Fei 
AU  - Dong, Zhen 

TI  - Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 79
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Intrusion detection; deep learning; spiral convolution; long and short term memory networks; 1D-spiral convolution

DO  - 10.32604/cmc.2024.048443