Network intrusion detection method based on improved FCM and rule parameter optimization in cloud environment

Chunqin ZANG; Lichun XIE

doi:10.11959/j.issn.1000-0801.2018005

您当前的位置：

首页 >

文章列表页 >

Network intrusion detection method based on improved FCM and rule parameter optimization in cloud environment

research and development | 更新时间：2024-06-05

- Network intrusion detection method based on improved FCM and rule parameter optimization in cloud environment
- Telecommunications Science Vol. 34, Issue 1, Pages: 72-79(2018)
- 作者机构：
  
  1. 浙江工业职业技术学院，浙江绍兴 312000
  2. 浙江工业大学，浙江杭州 310014
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018005
  CLC： TP393
- Published Online：2018-01，
  
  Published：20 January 2018
- 稿件说明：
移动端阅览
Chunqin ZANG, Lichun XIE. Network intrusion detection method based on improved FCM and rule parameter optimization in cloud environment[J]. Telecommunications science, 2018, 34(1): 72-79.
DOI：

Chunqin ZANG, Lichun XIE. Network intrusion detection method based on improved FCM and rule parameter optimization in cloud environment[J]. Telecommunications science, 2018, 34(1): 72-79. DOI： 10.11959/j.issn.1000-0801.2018005.

摘要

针对云环境中的网络入侵检测问题，提出一种基于模糊推理的网络入侵检测方法。首先，利用互信息特征选择对样本特征进行降维。然后，利用提出的改进模糊C均值聚类（IFCM）方法对训练样本集进行聚类，根据各样本特征与集群的对应关系获得初始模糊规则库。接着，对每个规则的前件参数和后件参数进行调优，以此获得准确的规则库。最后，基于规则库对输入连接数据进行模糊推理，对其进行分类以实现入侵检测。在云入侵检测数据集上的实验结果表明，该方法能够准确检测出网络入侵，具有可行性和有效性。

Abstract

Aiming at the network intrusion detection problem in cloud environment

a method of network intrusion detection based on fuzzy inference was proposed.Firstly

it used the mutual information feature selection to reduce the feature of the sample.Then

the improved fuzzy C-means clustering method was used to cluster the training sample set

and the initial fuzzy rule base was got by the correspondence between each sample feature and cluster.After that

the refine parameter and consequent parameters of each rule were tuned to obtain an exact rule base.Finally

fuzzy inference was carried out on the input connection data based on the rule base

and it was classified to realize intrusion detection.Experimental results on the cloud intrusion detection dataset show that this method can detect the network intrusion accurately

and it is feasible and effective.

关键词

Keywords

references

罗亮 , 吴文峻 , 张飞 . 面向云计算数据中心的能耗建模方法 [J ] . 软件学报 , 2014 , 25 ( 7 ): 1371 - 1387 .

LUO L , WU W J , ZHANG F . Energy modeling based on cloud data center [J ] . Journal of Software , 2014 , 25 ( 7 ): 1371 - 1387 .

FOSSACECA J M , MAZZUCHI T A , SARKANI S . MARK-ELM:application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection [J ] . Expert Systems with Applications , 2015 , 42 ( 8 ): 4062 - 4080 .

石云 , 陈钟 , 孙兵 . 基于均值聚类分析和多层核心集凝聚算法相融合的网络入侵检测 [J ] . 计算机应用研究 , 2016 , 32 ( 2 ): 518 - 520 .

SHI Y , CHEN Z , SUN B . Networks intrusion detection based on integrating k-mean clustering analysis and multilayer core-set agglomerative algorithm [J ] . Computer Applications and Software , 2016 , 32 ( 2 ): 518 - 520 .

陆婷婷 , 韩旭 . 面向 MANET 报文丢弃攻击的模糊入侵检测系统 [J ] . 电信科学 , 2016 , 32 ( 10 ): 124 - 129 .

LU T T , HAN X . Fuzzy intrusion detection system for MANET packet dropping attack [J ] . Telecommunications Science , 2016 , 32 ( 10 ): 124 - 129 .

SHAMSHIRBAND S , AMINI A , ANUAR N B , et al . D-FICCA:a density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks [J ] . Measurement , 2014 , 55 ( 9 ): 212 - 226 .

李洪成 , 吴晓平 , 陈燕 . MapReduce 框架下支持差分隐私保护的K-means聚类方法 [J ] . 通信学报 , 2016 , 37 ( 2 ): 124 - 130 .

LI H C , WU X P , CHEN Y . K-means clustering method preserving differential privacy in MapReduce framework [J ] . Journal on Communications , 2016 , 37 ( 2 ): 124 - 130 .

TIAN L , JIANWEN W . Research on network intrusion detection system based on improved K-means clustering algorithm [C ] // International Forum on Computer Science-Technology and Applications,Dec 25-27,2009,Chongqing,China . Piscataway:IEEE Press , 2009 : 76 - 79 .

XIE L , WANG Y , CHEN L , et al . An anomaly detection method based on fuzzy C-means clustering algorithm [J ] . Proceedings of the International Symposium on Networking ＆ Netwo , 2014 , 24 ( 5 ): 56 - 63 .

魏莎莎 , 陆慧娟 , 金伟 , 等 . 基于云平台的互信息最大化特征提取方法研究 [J ] . 电信科学 , 2013 , 29 ( 10 ): 38 - 42 .

WEI S S , LU H J , JIN W , et al . Maximum mutual information feature extraction method based on the cloud platform [J ] . Telecommunications Science , 2013 , 29 ( 10 ): 38 - 42 .

徐峻岭 , 周毓明 , 陈林 , 等 . 基于互信息的无监督特征选择 [J ] . 计算机研究与发展 , 2012 , 49 ( 2 ): 372 - 382 .

XU J L , ZHOU Y M , CHEN L , et al . An unsupervised feature selection approach based on mutual information [J ] . Journal of Computer Research and Development , 2012 , 49 ( 2 ): 372 - 382 .

AMIRI F , REZAEI YOUSEFI M , LUCAS C , et al . Mutual information-based feature selection for intrusion detection systems [J ] . Journal of Network ＆ Computer Applications , 2011 , 34 ( 4 ): 1184 - 1199 .

SONG J , ZHU Z , SCULLY P , et al . Selecting features for anomaly intrusion detection:a novel method using fuzzy C means and decision tree classification [J ] . Cyberspace Safety and Security , 2013 , 8 ( 3 ): 299 - 307 .

琚春华 , 鲍福光 , 戴俊彦 . 一种融入公众情感投入分析的微博话题发现与细分方法 [J ] . 电信科学 , 2016 , 32 ( 7 ): 97 - 105 .

JU C H , BAO F G , DAI J Y . Discovery and segmentation method in micro-blog topics based on public emotional engagement analysis [J ] . Telecommunications Science , 2016 , 32 ( 7 ): 97 - 105 .

LIU X , QIN Y , WU L . Fast and direct Karnik-Mendel algorithm computation for the centroid of an interval type-2 fuzzy set [C ] // IEEE International Conference on Fuzzy Systems,June 10-15,2012,Brisbane,QLD,Australia . Piscataway:IEEE Press , 2012 : 1 - 8 .

KHOLIDY H A , BAIARDI F . CIDD:a cloud intrusion detection dataset for cloud computing and masquerade attacks [C ] // Ninth International Conference on Information Technology-New Generations,April l16-18,2012,Las Vegas,NV,USA . Piscataway:IEEE Press , 2012 : 397 - 402 .

Views

677

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰