智能算法优化神经网络在网络安全的应用作者：何欢来源：《现代电子技术》2016年第20期 摘 要： 研究了一种智能优化神经网络算法的网络流量检测模型。使用QAPSO算法对中华人民共和国反法
RBF神经网络的基函数中心、基函数的宽度以及输出层与隐含层的连接权值进行优化。通过实例对该文研究的检测模型进行分析，使用采集的数据对网络流量识别系统进行训练和性能测试。将该文的研究方法和基于常规PSO算法、基于HPSO算法进行对比，结果表明，该文研究的检测方法具有更快的识别速度以及更好的识别准确率，避免了出现陷入局部最优解的情况发生。 许多余关键词： 网络流量检测； 智能算法； RBF神经网络；汽油清净剂 网络安全
失而复得的圣诞礼物中图分类号： TN926⁃34； TP393 文献标识码： A 文章编号： 1004⁃373X（第四范式2016）20⁃0012⁃03
Abstract： The application of swarm intelligence optimizing neural network in network
security and a network traffic detection model based on neural network algorithm are studied in this paper. QAPSO algorithm is used to optimize the basis function center and base function width of RBF neural network， and the connection weights of the output layer and the hidden layer as well. The detection model studied in this paper is analyzed by means of an example. The collected data is used to train the network traffic identification system and test its performance. The method researched in this paper is compared with the algorithms based on the conventional PSO and HPSO. The results show that the detection method has a faster recognition speed and better recognition accuracy， and can avoid the occurrence of local optimal solutions.
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