GHSOM intrusion detection based on Dempster-Shafer theory

被引：0

作者：

Su, Jie ^{[1
]}

Dong, Wei-Wei ^{[1
]}

Xu, Xuan ^{[1
]}

Liu, Shuai ^{[1
]}

Xie, Li-Peng ^{[1
]}

机构：

[1] School of Computer Science and Technology, Harbin University of Science and Technology, Harbin

来源：

Tongxin Xuebao/Journal on Communications | 2015年 / 36卷

关键词：

Dempster-Shafer theory; Incremental GHSOM neural networks; Intrusion detection; Network security;

D O I：

10.11959/j.issn.1000-436x.2015282

中图分类号：

学科分类号：

摘要：

On the basis of incremental GHSOM, the GHSOM neural network intrusion detection based on the theory of evidence reasoning method was put forward. It can deal with the uncertainty caused by randomness and fuzziness, as well as can constantly narrowing assumptions set by accumulate the evidence, effectively control dynamic growth of network and keep a good accuracy in noise environment. Experiments show that GHSOM intrusion detection method based on the Dempster Shafer theory realized the dynamic control for the scale of expended subnet during the process of detection. It has the better detection accuracy in the noise environment and improves the adaptability and extensibility of incremental GHSOM neural network intrusion detection method when the scale of network is expanded. © 2015, Editorial Board of Journal on Communications. All right reserved.

引用

页数：5

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