Research on WSNs intrusion detection technology based on PSO-RBF

YI Xiao-mei; WU Peng; LIU Li-juan; DAI Dan

doi:10.13873/j.1000-97872011.09.019

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Research on WSNs intrusion detection technology based on PSO-RBF

更新时间：2025-09-11

- Research on WSNs intrusion detection technology based on PSO-RBF
- Vol. 30, Issue 9, Pages: 9-11(2011)
- 作者机构：
  
  浙江农林大学信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.09.019
  CLC： TN915.08;TP212.9
- Published：2011
- 稿件说明：
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[1]易晓梅,吴鹏,刘丽娟,戴丹.基于PSO-RBF无线传感器网络入侵检测技术研究[J].传感器与微系统,2011,30(09):9-11.

YI Xiao-mei, WU Peng, LIU Li-juan, et al. Research on WSNs intrusion detection technology based on PSO-RBF[J]. 2011, 30(9): 9-11.
[1]易晓梅,吴鹏,刘丽娟,戴丹.基于PSO-RBF无线传感器网络入侵检测技术研究[J].传感器与微系统,2011,30(09):9-11. DOI： 10.13873/j.1000-97872011.09.019.

YI Xiao-mei, WU Peng, LIU Li-juan, et al. Research on WSNs intrusion detection technology based on PSO-RBF[J]. 2011, 30(9): 9-11. DOI： 10.13873/j.1000-97872011.09.019.

摘要

针对无线传感器网络自身特性

提出了基于粒子群优化（PSO）径向基函数（RBF）神经网络的轻量级入侵检测方案

该方案结合PSO算法与RBF神经网络分别在全局搜索和局部搜索的优势

使用PSO优化RBF的中心、宽度及权值。仿真实验表明:基于PSO-RBF的入侵检测算法可以有效、可靠地运用于无线传感器入侵检测系统中。

Abstract

A particle swarm optimization（PSO）

radial basis function（RBF）lightweight intrusion detection framework is proposed aiming at the features of WSNs.The parameters of RBF is optimized by assimilating the advantages both of PSO’s global search and RBF’s local search

the test result shows that PSO-RBF algorithm might be effectively and reliably used in WSNs intrusion detection system.

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references

基于粒子群优化RBF神经网络原油含水率预测 [J]. 吴良海. 计算机仿真 . 2010(05)

基于核聚类的无线传感器网络异常检测方案 [J]. 杨黎斌,慕德俊,蔡晓妍. 传感技术学报 . 2008(08)

一种无线传感器网络异常检测技术研究 [J]. 周贤伟,王培,覃伯平,申吉红. 传感技术学报 . 2007(08)

无线传感器网络 [J]. 任丰原,黄海宁,林闯. 软件学报 . 2003(07)

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