Department of Information Technology and College of Computer and Information Sciences, King Saud University, Riyadh Saudi Arabia.
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh Saudi Arabia.
Big Data. 2021 Jun;9(3):233-252. doi: 10.1089/big.2020.0263.
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 and 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.
网络安全可保护计算机系统和网络免受网络攻击，并能从中恢复。随着人们对技术的依赖程度不断提高，网络安全的重要性也相应增加。基于异常检测的网络入侵检测系统是计算机网络安全框架中必不可少的。在本文中，我们提出了两种基于深度学习的模型，用于解决网络攻击的二进制和多类分类问题。我们的模型使用卷积神经网络架构。此外，还提出了一种混合的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 和 ，使用深度特征合成。使用两个基准数据集，即网络安全实验室-数据库知识发现数据集和新南威尔士大学基于 2015 年的网络数据集，评估了我们模型的性能。将其性能与文献中的类似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，我们的模型在准确性和召回率方面表现良好，优于文献中的类似模型。
