Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network. In this article, we propose two models based on deep learning to address the binary and multiclass classification of network attacks. We use a convolutional neural network architecture for our models. In addition, a hybrid two-step preprocessing approach is proposed to generate meaningful features. The proposed approach combines dimensionality reduction and feature engineering using deep feature synthesis. The performance of our models is evaluated using two benchmark data sets, namely the network security laboratory-knowledge discovery in databases data set and the University of New South Wales Network Based 2015 data set. The performance is compared with similar deep learning approaches in the literature, as well as state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall, outperforming similar models in the literature.

中文翻译：

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用于改进基于异常的网络入侵检测的卷积神经网络

网络安全保护和恢复计算机系统和网络免受网络攻击。随着人们越来越依赖技术，网络安全的重要性也在不断增长。基于异常检测的网络入侵检测系统对于计算机网络中的任何安全框架都是必不可少的。在本文中，我们提出了两种基于深度学习的模型来解决网络攻击的二分类和多分类问题。我们为我们的模型使用卷积神经网络架构。此外，提出了一种混合两步预处理方法来生成有意义的特征。所提出的方法结合了降维和特征工程使用深度特征合成。我们模型的性能是使用两个基准数据集评估的，即数据库数据集中的网络安全实验室知识发现数据集和新南威尔士大学基于网络的 2015 数据集。性能与文献中类似的深度学习方法以及最先进的分类模型进行了比较。实验结果表明，我们的模型在准确率和召回率方面取得了良好的性能，优于文献中的类似模型。