Comparative Study of CNN and RNN for Deep Learning Based Intrusion Detection System

被引：13
|
作者：
Cui, Jianjing [1 ]
Long, Jun [1 ]
Min, Erxue [1 ]
Liu, Qiang [1 ]
Li, Qian [2 ]
机构：
[1] Natl Univ Def Technol, Dept Comp Sci, Changsha 410005, Hunan, Peoples R China
[2] Univ Technol Sydney, Fac Engn & IT, Sydney, NSW 2007, Australia
来源：
CLOUD COMPUTING AND SECURITY, PT V | 2018年 / 11067卷
基金：
中国国家自然科学基金;
关键词：
Intrusion detection system; Deep neural networks; Convolutional neural network; Recurrent neural network;
D O I：
10.1007/978-3-030-00018-9_15
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Intrusion detection system plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. Due to huge increase in network traffic and different types of attacks, accurately classifying the malicious and legitimate network traffic is time consuming and computational intensive. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), the two main types of DNN architectures, are widely explored to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of CNN and RNN on the deep learning based intrusion detection systems, aiming to give basic guidance for DNN selection.
引用
收藏
页码：159 / 170
页数：12
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