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.

中文翻译：

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基于层次特征的入侵检测方法研究与应用

入侵检测对于防止损坏计算机系统至关重要。然而近年来，随着网络的发展，出现了许多复杂的攻击类型，想要获得高检测率和低误报率变得越来越困难。此外，用于网络入侵检测的传统大量手工制作的评估数据集并不实用。本文提出了一种基于分层特征学习的入侵检测方法，可以自动学习流量特征。该方法首先通过一维卷积神经网络学习网络流量的字节级特征，然后使用堆叠去噪自编码器学习会话级特征。实验分析了模型结构，并与其他方法进行了比较。