计算机科学 ›› 2011, Vol. 38 ›› Issue (7): 70-72.

• 计算机网络与信息安全 • 上一篇    下一篇

基于HHGA-RBF神经网络的网络安全态势预测模型

孟 锦,马 驰,何加浪,张 宏   

  1. (南京理工大学计算机科学与技术学院623教研室 南京 210094)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家白然科学基金项目(90718021,60903027),白主科研先期投人计划(2010XQTR04)资助。

Network Security Situation Prediction Model Based on HHGA-RBF Neural Network

MEND Jin,MA Chi,HE Jia-lang,ZHANG Hong   

  • Online:2018-11-16 Published:2018-11-16

摘要: 针对网络安全态势感知中的预测问题,提出了采用径向基函数(R13F)神经网络对态势值进行预测的方法。为了提高RI3F神经网络的预测精度,使用混合递阶遗传算法(HHGA)对RI3F神经网络进行训练,获得了神经网络结构参数。实验结果说明了此预测方法的有效性,并通过与已有的预测方法进行对比实验,验证了所提算法在精度方面的优越性。

关键词: 混合递阶遗传算法,网络安全态势,态势预测,RI3F神经网络

Abstract: To address the prediction problem in the Network Security Situational Awareness, the RI3F neural network method was proposed to predict the situation value. In order to improve the prediction accuracy of RBF neural network,the HHUA was used for training to acquire the neural network parameters. Experimental results show the validity of this prediction method,and the accuracy of the proposed algorithm was verified by comparative experiment with the existing forecasting methods.

Key words: HHGA, Network security situation, Situation prediction, RI3F neural network

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