An intrusion detection system (IDS) is designed to detect and analyze network traffic for suspicious activity. Several methods have been introduced in the literature for IDSs; however, due to a large amount of data, these models have failed to achieve high accuracy. A statistical approach is proposed in this research due to the unsatisfactory results of traditional intrusion detection methods. The features are extracted and selected using a multilayer convolutional neural network, and a softmax classifier is employed to classify the network intrusions. To perform further analysis, a multilayer deep neural network is also applied to classify network intrusions. Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed model is evaluated using four performance metrics: accuracy, recall, F1-score, and precision. The experimental results show that the proposed approach achieved better accuracy (99%) compared with other IDSs.

中文翻译：

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一种使用混合多层深度学习模型的网络入侵检测系统

入侵检测系统 (IDS) 旨在检测和分析网络流量中的可疑活动。文献中介绍了几种用于 IDS 的方法；然而，由于数据量大，这些模型未能达到高精度。由于传统入侵检测方法的结果不理想，本研究提出了一种统计方法。使用多层卷积神经网络提取和选择特征，并使用softmax分类器对网络入侵进行分类。为了进行进一步分析，还应用了多层深度神经网络来对网络入侵进行分类。此外，实验是使用两个常用的基准入侵检测数据集进行的：NSL-KDD 和 KDDCUP'99。使用四个性能指标评估所提出模型的性能：准确度、召回率、F1 分数和精度。实验结果表明，与其他 IDS 相比，所提出的方法实现了更好的准确度（99%）。