论文标题
对计算机网络流量分类的深神经网络的审查
A review on Deep Neural Network for Computer Network Traffic Classification
论文作者
论文摘要
专注于基于神经网络的恶意和普通计算机网络流量分类。 （例如攻击，网络钓鱼，任何其他非法活动和正常的交通识别）。在本文中，主要思想是审查，基于神经网络的网络流量分类。这表明入侵活动分类和检测。对网络流量进行分类以保护连接到计算机网络的任何系统非常重要。它有多种NN体系结构，其准确率不同。在本文中，我们将在它们之间进行相对压缩。 Index Terms-Computer Network, Network traffic, Packet, Intrusion, DOS (Denial-of-service), unauthorized access, IDS (Intrusion Detection System), IPS (Intrusion Prevention Systems), R2L (Remote to Local Attack), Probing, U2R (User to Root Attack), DNN (Deep Neural Network), CRNN (Convolutional Recurrent Neural Network), RPROP (Resilient propagation).
Focus on Deep Neural Network based malicious and normal computer Network Traffic classification. (such as attacks, phishing, any other illegal activity and normal traffic identification). In this paper, the main idea is to review, existed Neural Network based network traffic classification. Which indicates intrusion activity classification and detection. It is very important to classify network traffic to safeguard any system, connected to computer network. There are a variety of NN architecture for it, with different rate of accuracy. On this paper we will do relative compression among them. Index Terms-Computer Network, Network traffic, Packet, Intrusion, DOS (Denial-of-service), unauthorized access, IDS (Intrusion Detection System), IPS (Intrusion Prevention Systems), R2L (Remote to Local Attack), Probing, U2R (User to Root Attack), DNN (Deep Neural Network), CRNN (Convolutional Recurrent Neural Network), RPROP (Resilient propagation).
