Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling
Fei Wang, Zhen Xing Dong · Computers, materials & continua/Computers, materials & continua (Print) · 2024
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.