Application of Improved BP Neural Network with Correlation Rules in Network Intrusion Detection

被引：5
|
作者：
Cui, Yongfeng [1 ]
Ma, Xiangqian Li [2 ]
Liu, Zhijie [3 ]
机构：
[1] Zhoukou Normal Univ, Sch Sci & Technol, Zhoukou 466001, Henan, Peoples R China
[2] Zhoukou Vocat & Tech Coll, Network Ctr, Zhoukou 466000, Henan, Peoples R China
[3] Zhoukou Normal Univ, Lib, Zhoukou 466001, Henan, Peoples R China
来源：
INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS | 2016年 / 10卷 / 04期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
network intrusion detection; BP neural network; correlation rules; anomaly network traffic;
D O I：
10.14257/ijsia.2016.10.4.37
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
To detect various network attacks in real time, this paper developed a network intrusion detection system based on artificial neural network. This paper first introduced the recent development of neural network, BP algorithm and structure of a simple perceptron. Then, this paper developed an improved BP neural network algorithm to detect anomaly network traffic with adjusted correlation rules. Finally, the network intrusion system in this paper was tested in a real network situation; the improved BP algorithm neural network with adjusted correlation rules shows a reduction in total error and increment in alarm rate compared to the traditional basic BP algorithm model.
引用
收藏
页码：423 / 430
页数：8
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