GHSOM intrusion detection based on Dempster-Shafer theory

Jie SU; Wei-wei DONG; Xuan XU; Shuai LIU; Li-peng XIE

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

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GHSOM intrusion detection based on Dempster-Shafer theory

Academic paper | 更新时间：2024-06-05

- GHSOM intrusion detection based on Dempster-Shafer theory
- Journal on Communications Vol. 36, Issue Z1, Pages: 60-64(2015)
- 作者机构：
  
  哈尔滨理工大学计算机科学与技术学院，黑龙江哈尔滨 150080
- 作者简介：
- 基金信息：
  
  The Natural Science Foundation of Heilongjiang Province(A201301);Scientific Planning Issues of Education in Heilongjiang Province(GBC1211062);Research Fund for the Program of New Century Excellent Talents in Heilongjiang Provincial University(1155-ncet-008);Post Doctoral Fund of Heilongjiang(LBH-Z12082)
- DOI：10.11959/j.issn.1000-436x.2015282
  CLC： TP309
- Online First：2015-11，
  
  Published：25 November 2015
- 稿件说明：
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Jie SU, Wei-wei DONG, Xuan XU, et al. GHSOM intrusion detection based on Dempster-Shafer theory[J]. Journal on Communications, 2015, 36(Z1): 60-64.
DOI：

Jie SU, Wei-wei DONG, Xuan XU, et al. GHSOM intrusion detection based on Dempster-Shafer theory[J]. Journal on Communications, 2015, 36(Z1): 60-64. DOI： 10.11959/j.issn.1000-436x.2015282.

摘要

结合证据推理DS理论，提出了基于Dempster-Shafer理论的GHSOM神经网络入侵检测方法，一方面处理数据不确定性中的随机性和模糊性问题，可以在噪音环境下保持良好的检测率，此外通过证据融合理论缩小数据集，有效控制网络的动态增长。实验结果表明，基于 Dempster-Shafer 理论的 GHSOM 入侵检测方法实现了对子网拓展规模在检测中的动态控制，提升了在网络规模不断扩展时的动态适应性，在噪音环境下具有良好的检测准确率，提升了GHSOM入侵检测方法的扩展性。

Abstract

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.

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references

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