J4 ›› 2015, Vol. 42 ›› Issue (4): 107-113.doi: 10.3969/j.issn.1001-2400.2015.04.018

• 研究论文 • 上一篇    下一篇



  1. (国家数字交换系统工程技术研究中心,河南 郑州  450002)
  • 收稿日期:2014-03-12 出版日期:2015-08-20 发布日期:2015-10-12
  • 通讯作者: 葛琳
  • 作者简介:葛琳(1978-),女,国家数字交换系统工程技术研究中心博士研究生,E-mail: lingesnow@126.com.
  • 基金资助:


Situation prediction of network information content  security incidents using E-IGA-RBF

GE Lin;JI Xinsheng;JIANG Tao   

  1. (National Digital Switching System Engineering and Technological Research Center, Zhengzhou  450002, China)
  • Received:2014-03-12 Online:2015-08-20 Published:2015-10-12
  • Contact: GE Lin



关键词: 信息内容安全事件, 态势预测, 径向基函数神经网络, 免疫遗传算法, 精英选择模型, 模拟退火算法


In order to resolve the problem of optimizing RBF, an elitist model-immune genetic algorithm is put forward to optimize the structure and parameters of the RBF neural network. The model uses elite selection strategy and adds the factor of simulated annealing. It ensures good genes to be retained into the next generation. At the same time, it increases the diversity of variation to a certain extent through the disturbance of the annealing factor. And the model improves the convergence rate and local search capacity of the whole algorithm. Experimental results are used to demonstrate the effectiveness and reliability of the algorithm when predicting the situation of network information content security incidents.

Key words: information content security incidents, situation prediction, radial-basis-function(RBF) neural network, immune genetic algorithm(IGA), elitist selection model, simulated annealing algorithm


  • TP391