Intrusion detection system (IDS) plays a significant role in preventing network attacks and plays a vital role in the field of national security. At present, the existing intrusion detection methods are generally based on traditional machine learning models, such as random forest and decision tree, but they rely heavily on artificial feature extraction and have relatively low accuracy. To solve the problems of feature extraction and low detection accuracy in intrusion detection, an intrusion detection model SAAE-DNN, based on stacked autoencoder (SAE), attention mechanism and deep neural network (DNN), is proposed. The SAE represents data with a latent layer, and the attention mechanism enables the network to obtain the key features of intrusion detection. The trained SAAE encoder can not only automatically extract features, but also initialize the weights of DNN potential layers to improve the detection accuracy of DNN. We evaluate the performance of SAAE-DNN in binary-classification and multi-classification on an NSL-KDD dataset. The SAAE-DNN model can detect normally and attack symmetrically, with an accuracy of 87.74% and 82.14% (binary-classification and multi-classification), which is higher than that of machine learning methods such as random forest and decision tree. The experimental results show that the model has a better performance than other comparison methods.

中文翻译：

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SAAE-DNN：入侵检测深度学习方法

入侵检测系统（IDS）在防止网络攻击方面发挥着重要作用，在国家安全领域发挥着至关重要的作用。目前，现有的入侵检测方法一般基于传统的机器学习模型，如随机森林、决策树等，但严重依赖人工特征提取，准确率较低。针对入侵检测中特征提取和检测精度低的问题，提出了一种基于堆叠自编码器（SAE）、注意力机制和深度神经网络（DNN）的入侵检测模型SAAE-DNN。SAE 用潜在层表示数据，注意力机制使网络能够获得入侵检测的关键特征。经过训练的 SAAE 编码器不仅可以自动提取特征，还要初始化 DNN 潜在层的权重以提高 DNN 的检测精度。我们在 NSL-KDD 数据集上评估了 SAAE-DNN 在二分类和多分类中的性能。SAAE-DNN模型可以正常检测和对称攻击，准确率分别为87.74%和82.14%（二分类和多分类），高于随机森林和决策树等机器学习方法。实验结果表明，该模型比其他比较方法具有更好的性能。14%（二分类和多分类），高于随机森林和决策树等机器学习方法。实验结果表明，该模型比其他比较方法具有更好的性能。14%（二分类和多分类），高于随机森林和决策树等机器学习方法。实验结果表明，该模型比其他比较方法具有更好的性能。