关键词: 堆叠式降噪自编码器（SDA）, 自适应过采样算法（ADASYN）, 深度学习, 入侵检测

Abstract: Aiming at the poor detection performance of traditional intrusion detection models in high-dimensional data and data imbalance environment, an intrusion detection model combining Adaptive Synthetic Sampling Approach（ADASYN） with improved Stacked Denoising Autoencoder（SDA） is proposed. Firstly, the data oversampling process is performed using the ADASYN approach. Secondly, the Adam optimization approach and Dropout regularization are used to improve the SDA deep learning model, and the integration features of low dimensionality and high robustness are extracted. Finally, intrusion detection is performed in the softmax classifier. Experimental results show that compared with SDA, AE-DNN and MSVM, the ADASYN-SDA model has a certain degree of improvements in average accuracy, detection rate and false positive rate.

Key words: Stacked Denoise Autoencoder（SDA）, Adaptive Synthetic Sampling Approach（ADASYN）, deep learning, intrusion detection