Intrusion Detection Model Combining Improved Self-Encoder and Residual Network
The vast quantities of private data on the Internet necessitate robust network intrusion prevention to safeguard network security.This study introduces an Improve Stacked AutoEncoder-ResNet(ISAE-ResNet),combining an improved self-encoder and residual network(ISAE-ResNet),to augment network intrusion detection accuracy and address the challenge of slow convergence.The model integrates an improved stack self-encoder with the Residual Network(ResNet).Initially,preprocessed data are fed into the enhanced stack self-encoder,consisting of two sub-encoders and a main encoder.By training these components,data is reconstructed with novel features to mitigate fitting issues.The advanced stack self-encoder synchronizes the weights of the decoding and encoding layers,thereby reducing model parameters by half,diminishing dimensionality,and accelerating convergence.Subsequently,the processed data is introduced into the refined ResNet,incorporating a residual module equipped with a soft threshold function to enhance accuracy by diminishing data noise.The model's efficacy and feasibility are evaluated using the CIC Intrusion Detection Systems-2017 dataset(CIC-IDS-2017).Results demonstrated a 98.67%accuracy rate,a 95.93%true case rate,a mere 0.37%false alarm rate,and rapid convergence of the loss function value to 0.042.These metrics surpass existing models in terms of accuracy,true case rate,false alarm rate,and convergence speed,thereby affirming the high validity and feasibility of the proposed intrusion detection model.
国家科技期刊平台
NSTL
万方数据
维普
