魏雅娟;刘兆;刘意先;范九伦;

• 1:西安邮电大学网络空间安全学院

摘要(Abstract)：

针对网络态势预测方法预测精度不高的问题,提出一种动态K-means与粒子群的网络态势预测优化算法。利用动态K-means聚类算法对收集到的网络基础指标进行分类,确定径向基函数(radial basis function,RBF)的网络中心和神经元宽度,使用粒子群最优化算法调整并确定RBF的权值系数,使权值分配更为合理,从而提高计算某局域网在某个时间段内所得态势预测值的精度。仿真结果表明,所提预测算法比基于广义的RBF网络态势预测算法的预测精度提高了14倍。

关键词(KeyWords)： 动态K-means聚类;径向基函数;粒子群最优化算法;态势预测;预测精度

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(61671377,61601362)

作者(Author): 魏雅娟;刘兆;刘意先;范九伦;

Email:

DOI: 10.13682/j.issn.2095-6533.2020.05.006

参考文献(References)：

• [1] BASS T.Intrusion detection systems and multisensor data fusion[J].Communications of the ACM,2000,43(4):99-105.
• [2] SRIHARI R K.Situation awareness through concept-based information extraction[EB/OL].[2019-08-20].http://www.dawnbreaker.com/vas05.
• [3] LAU S.The spinning cube of potential doom[J].Communications of the ACM,2004,47(6):25-26.DOI:10.1145/990680.990699
• [4] LIU Z M,LI S,HE J,et al.Complex network security analysis based on attack graph model[C]//Proceedings of the 2012 Second International Conference on Instrumentation,Measurement,Computer,Communication and Control.Harbin:IEEE,2012:183-186.DOI:10.1109/IMCCC.2012.50.
• [5] 肖汉杰,桑秀丽.相关向量机超参数优化的网络安全态势预测[J].计算机应用,2015,35(7):1888-1891.DOI:10.11772/j.issn.1001-9081.2015.07.1888.
• [6] 范九伦,伍鹏.基于RBF神经网络的网络安全态势预测方法[J].西安邮电大学学报,2017,22(2):7-11.DOI:10.13682/j.issn.2095-6533.2017.02.002.
• [7] 程学旗,靳小龙,王元卓,等.大数据系统和分析技术综述[J].软件学报,2014,25(9):1889-1908.DOI:10.13328/j.cnki.jos.004674.
• [8] 任伟,蒋兴浩,孙锬锋.基于RBF神经网络的网络安全态势预测方法[J].计算机工程与应用,2006(31):140-142.DOI:10.3321/j.issn:1002-8331.2006.31.041.
• [9] 薛丽敏,李忠,蓝湾湾.基于在线学习RBFNN的网络安全态势预测技术研究[J].信息网络安全,2016(4):23-30.
• [10] 国务院.国家突发公共事件总体应急预案[M].北京:中国法制出版社,2006.
• [11] 李方伟,张新跃,朱江,等.基于信息融合的网络安全态势评估模型[J].计算机应用,2015,35(7):1882-1887.DOI:10.11772/j.issn.1001-9081.2015.07.1882.
• [12] 田森平,吴文亮.自动获取k-means聚类参数k值的算法[J].计算机工程与设计,2011,32(1):274-276.
• [13] 吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3):416-420.DOI:10.3321/j.issn:0372-2112.2004.03.016.
• [14] 陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,40(1):53-56.DOI:10.3321/j.issn:0253-987X.2006.01.013.
• [15] 黄磊.粒子群优化算法综述[J].机械工程与自动化,2010(5):205-207.DOI:10.3969/j.issn.1672-6413.2010.05.076.
• [16] 甘文道,周城,宋波.基于RAN-RBF神经网络的网络安全态势预测模型[J].计算机科学,2016,43(s2):388-392.
• [17] 谢丽霞,王亚超,于巾博.基于神经网络的网络安全态势感知[J].清华大学学报(自然科学版),2013(12):1750-1760.
• [18] LEAU Y B,MANICKAM S.Network aecurity situation prediction:A review and discussion[C]//Proceedings of theInternational Conference on Soft Computing,Intelligence Systems,and Information Technology.Bali:Springer,2015:424-435.
• [19] XU G Q,CAO Y,REN Y Y,et al.Network security situation awareness based on semantic ontology and user-defined rules for internet of things[J].IEEE Access,2017(99):21046-21056.DOI:10.1109/ACCESS.2017.2734681.
• [20] ARI C,AKSOY S.Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization[C]//Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium.Honolulu:IEEE,2010:1859-1862.DOI:10.1109/IGARSS.2010.5653855.
• [21] LIU Z,LI S,HE J,et al.Complex network security analysis based on attack graph model[C]//Proceedings of the 2012 Second International Conference on Instrumentation,Measurement,Computer,Communication and Control.Harbin:IEEE,2012:183-186.DOI:10.1109/IMCCC.2012.50.
• [22] HU G Y,QIAO P L.Cloud belief rule base model for network security situation prediction[J].IEEE Communications Letters,2016,20(5):914-917.DOI:10.1109/LCOMM.2016.2524404.
• [23] ZHANG Y X,JIN S Y,CUI X,et al.Network security situation prediction based on BP and RBF neural network[C]//Proceedings of the International Conference on Trustworthy Computing and Services.Beijing:Springer,2013:659-665.
• [24] 付凯,夏靖波,申健.基于复杂网络拓扑性质的网络态势预测方法[J].华中科技大学学报(自然科学版),2018,46(1):42-48.DOI:10.13245/j.hust.180109.
• [25] SHI Y,LI R,ZHANG Y,et al.An immunity-based time series prediction approach and its application for network security situation[J].Intelligent Service Robotics,2015,8(1):1-22.