Network intrusion detection algorithm based on deep neural network

被引：64

作者：

Jia, Yang ^{[1
]}

Wang, Meng ^{[1
]}

Wang, Yagang ^{[1
]}

机构：

[1] Xian Univ Posts & Telecommun, Sch Comp Sci, Changan West St Changan Dist, Xian, Shaanxi, Peoples R China

来源：

IET INFORMATION SECURITY | 2019年 / 13卷 / 01期

基金：

中国国家自然科学基金;

关键词：

neural nets; security of data; network intrusion detection algorithm; network technology; intrusion features; NSL-KDD training data; NSL-KDD dataset; NDNN model; deep neural network model; KDD99 training data; EXTREME LEARNING-MACHINE; FEATURE-SELECTION; DETECTION SYSTEM;

D O I：

10.1049/iet-ifs.2018.5258

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the rapid development of network technology, active defending of the network intrusion is more important than before. In order to improve the intelligence and accuracy of network intrusion detection and reduce false alarms, a new deep neural network (NDNN) model based intrusion detection method is designed. A NDNN with four hidden layers is modelled to capture and classify the intrusion features of the KDD99 and NSL-KDD training data. Experiments on KDD99 and NSL-KDD dataset shows that the NDNN-based method improves the performance of the intrusion detection system (IDS) and the accuracy rate can be obtained as high as 99.9%, which is higher when compared with other dozens of intrusion detection methods. This NDNN model can be applied in IDS to make the system more secure.

引用

页码：48 / 53

页数：6

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