Aimed at the existing problems in network intrusion detection, this paper proposes an improved LSTM combined with spatiotemporal structure for intrusion detection. The unsupervised spatiotemporal encoder is used to intelligently extract the spatial characteristics of network traffic data samples. It can not only retain the overall/nonlocal characteristics of the data samples but also extract the most essential deep features of the data samples. Finally, the extracted features are used as input of the LSTM model to realize classification and identification for intrusion samples. Experimental verification shows that the accuracy and false alarm rate of the intrusion detection model based on the neural network are significantly better than those of other traditional models.

中文翻译：

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结合多种时空结构的基于改进的长期记忆模型的网络入侵检测

针对网络入侵检测中存在的问题，提出一种结合时空结构的改进的LSTM进行入侵检测。无监督的时空编码器用于智能地提取网络流量数据样本的空间特征。它不仅可以保留数据样本的整体/非局部特征，还可以提取数据样本最重要的深层特征。最后，将提取的特征用作LSTM模型的输入，以实现对入侵样本的分类和识别。实验验证表明，基于神经网络的入侵检测模型的准确性和误报率明显优于其他传统模型。