Research and application of intrusion detection method based on hierarchical features
Xin Xie, Xunyi Jiang, Weiru Wang, Bin Wang, Tiancheng Wan, Wenliang Tang, Xianmin Wang · Concurrency and Computation Practice and Experience · 2020
Summary Intrusion detection is essential to prevent damage to computer systems. However, in recent years, with the development of the network, many complex attack types have appeared, and it has become increasingly difficult to obtain high detection rates and low false alarm rates. In addition, traditional heavily hand‐crafted evaluation datasets for network intrusion detection have not been practical. This article proposes an intrusion detection method based on hierarchical feature learning, which can automatically learn traffic features. The method first learns the byte‐level features of network traffic through one‐dimensional convolutional neural networks and then learns session‐level features using stacked denoising autoencoder. The experiment analyzed the model structure and compared it with other methods. Experiments prove that the method in this article has high accuracy and low false alarm rate.