桂林电子科技大学学报2024，Vol.44Issue(6)：621-627,7.DOI:10.16725/j.1673-808X.2021204
结合堆栈自编码器和FSVM的入侵检测方法
Intrusion detection method combining stacked autoencoder and FSVM
摘要
Abstract
Large-scale,high-dimensional sample data have a strong impact on the learning speed and learning effect of the 1,so an in-trusion detection method combining stacked auto-encoder and fast support vector machine(SAE-FSVM)was come up for discus-sion.A dimensionality reduction model based on stacked auto-encoder was built,through which the network data was extracted with features to obtain high-quality feature data.The clustering algorithm was used to cluster different types of data in the samples sepa-rately.The similarity of clusters between different types of samples was calculated to obtain similar clusters between different types of samples,and the sample data in the similar clusters were used as the input of the support vector machine model,so as to reduce the scale of the input samples and improved the learning speed of the model.Through simulation experiments,it is verified that this method can effectively improve the learning speed and learning effect of the support vector machine model for large-scale high-di-mensional data.关键词
支持向量机(SVM)/特征提取/样本规模归约/堆叠自编码器/入侵检测Key words
support vector machine(SVM)/feature extraction/sample size reduction/stacked auto-encoder/intrusion detection分类
信息技术与安全科学引用本文复制引用
张海洋,张斌..结合堆栈自编码器和FSVM的入侵检测方法[J].桂林电子科技大学学报,2024,44(6):621-627,7.基金项目
国家自然科学基金(61762028) （61762028）
