[1]胡波. 基于簇的移动自组网的IDMEF数据模型设计[J].计算机技术与发展,2016,26(08):93-97.
　HU Bo. Design of Data Model Intrusion of Detection Message Exchange Format in Wireless Ad Hoc Networks Based on Clusters[J].,2016,26(08):93-97.
点击复制

 基于簇的移动自组网的IDMEF数据模型设计()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年08期
页码:
93-97
栏目:
安全与防范
出版日期:
2016-08-10

文章信息/Info

Title:
 Design of Data Model Intrusion of Detection Message Exchange Format in Wireless Ad Hoc Networks Based on Clusters
文章编号:
1673-629X（2016)08-0093-05
作者:
 胡波
 福建省财政信息中心
Author(s):
 HU Bo
关键词:
 ; 入侵检测; 入侵检测信息交换格式; 入侵检测交换格式工作组; 移动自组网; 簇
Keywords:
 ; intrusion detection; IDMEF; IDWG; ad hoc networks; cluster
分类号:
TP31
文献标志码:
A
摘要:
 IDWG提出的IDMEF数据模型是基于有线网络的入侵检测而设计提出的，而移动自组网具有网络自组性、拓扑结构高度动态、传输带宽有限、移动节点局限性、多跳路由通信、分布式控制等独特特征，其安全性特别脆弱，加上文中欲设计基于簇的移动自组网入侵检测系统，导致该系统无法使用IDWG提出的IDMEF数据模型。因此，在综合考虑上述因素和参照原IDMEF数据模型的基础上，文中提出和设计了一种新的基于簇的移动自组网的IDMEF数据模型，并对其进行了详述。该模型能很好地适应移动自组网和本移动自组网入侵检测系统的需求。
Abstract:
 The data model of Intrusion Detection Message Exchange Format ( IDMEF) is put forward by IDWG which is designed in view of the cable network intrusion detection. Mobile ad-hoc network with network configuration,highly dynamic topology,limited trans-mission bandwidth,limitation of mobile node and multiple hops routing communication,distributed control and so on,its safety is particu-larly vulnerable,and it tries to design a mobile ad-hoc network intrusion detection system based on cluster in this paper,which leads to the system can’ t use IDMEF data model. Therefore,in consideration of the above factors and on the basis of reference of the original ID-MEF data model,a new IDMEF data model of mobile ad-hoc network based on cluster is put forward,and it is described in detail. The model can be a very good to adapt to the system requirements of mobile ad-hoc network and proposed one.

相似文献/References:

[1]李雷 丁亚丽 罗红旗.基于规则约束制导的入侵检测研究[J].计算机技术与发展,2010,(03):143.
　LI Lei,DING Ya-li,LUO Hong-qi.Intrusion Detection Technology Research Based on Homing - Constraint Rule[J].,2010,(08):143.
[2]马志远,曹宝香.改进的决策树算法在入侵检测中的应用[J].计算机技术与发展,2014,24(01):151.
　MA Zhi-yuan,CAO Bao-xiang.Application of Improved Decision Tree Algorithm in Intrusion Detection System[J].,2014,24(08):151.
[3]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
　GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].,2010,(08):148.
[4]林英 张雁 欧阳佳.日志检测技术在计算机取证中的应用[J].计算机技术与发展,2010,(06):254.
　LIN Ying,ZHANG Yan,OU Yang-jia.Application of Log Testing Technology in Computer Forensics[J].,2010,(08):254.
[5]李钦 余谅.基于免疫遗传算法的网格入侵检测模型[J].计算机技术与发展,2009,(05):162.
　LI Qin,YU Liang.Grid Intrusion Detection Model Based on Immune Genetic Algorithm[J].,2009,(08):162.
[6]黄世权.网络存储安全分析[J].计算机技术与发展,2009,(05):170.
　HUANG Shi-quan.Analysis of Network Storage＇s Safety[J].,2009,(08):170.
[7]李睿 肖维民.基于孤立点挖掘的异常检测研究[J].计算机技术与发展,2009,(06):168.
　LI Rui,XIAO Wei-min.Research on Anomaly Intrusion Detection Based on Outlier Mining[J].,2009,(08):168.
[8]胡琼凯 黄建华.基于协议分析和决策树的入侵检测研究[J].计算机技术与发展,2009,(06):179.
　HU Oiong-kai,HUANG Jian-hua.Intrusion Detection Based on Protocol Analysis and Decision Tree[J].,2009,(08):179.
[9]汪世义.基于优化支持向量机的网络入侵检测技术研究[J].计算机技术与发展,2009,(07):177.
　WANG Shi-yi.Network Intrusion Detection Based on Improved Support Vector Machine[J].,2009,(08):177.
[10]薛俊 陈行 陶军.一种基于神经网络的入侵检测技术[J].计算机技术与发展,2009,(08):148.
　XUE Jun,CHEN Hang,TAO Jun.Technology of Intrusion Detection Based on Neural Network[J].,2009,(08):148.
[11]李蓉,周维柏. 基于多特征选取和类完全加权的入侵检测[J].计算机技术与发展,2014,24(07):145.
　LI Rong,ZHOU Wei-bai. Intrusion Detection Based on Multiple Feature Selection and Class Fully Weighted [J].,2014,24(08):145.
[12]李锋. 粒子群模糊聚类算法在入侵检测中的研究[J].计算机技术与发展,2014,24(12):138.
　LI Feng. Research on Fuzzy Clustering Algorithm Based on PSO in IDS[J].,2014,24(08):138.
[13]朱俚治. 一种基于误用检测的新算法[J].计算机技术与发展,2015,25(02):135.
　ZHU Li-zhi. A New Algorithm Based on Misuse Detection[J].,2015,25(08):135.
[14]陈桂林,王生光,徐静妹,等. 基于GA和组合核的SVM入侵检测算法[J].计算机技术与发展,2015,25(02):148.
　CHEN Gui-lin,WANG Sheng-guang,XU Jing-mei,et al. Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function[J].,2015,25(08):148.
[15]张公让,万飞. 基于网格搜索的 SVM 在入侵检测中的应用[J].计算机技术与发展,2016,26(01):97.
　ZHANG Gong-rang,WAN Fei. Application of Support Vector Machine in Network Intrusion Detection Based on Grid Search[J].,2016,26(08):97.
[16]刘华春,候向宁,杨忠. 基于改进K均值算法的入侵检测系统设计[J].计算机技术与发展,2016,26(01):101.
　LIU Hua-chun,HOU Xiang-ning,YANG Zhong. Design of Intrusion Detection System Based on Improved K-means Algorithm[J].,2016,26(08):101.
[17]何文河[],李陶深[][],黄汝维[][]. 云环境下基于改进BP算法的入侵检测模型[J].计算机技术与发展,2016,26(02):87.
　HE Wen-he[],LI Tao-shen[][],HUANG Ru-wei[][]. Intrusion Detection Model Based on Improved BP Algorithm in Cloud Environment[J].,2016,26(08):87.
[18]牛永洁,薛宁静. 改进的免疫克隆算法在入侵检测中的应用[J].计算机技术与发展,2016,26(05):86.
　NIU Yong-jie,XUE Ning-jing. Application of Improved Immune Clonal Selection Algorithm in Intrusion Detection[J].,2016,26(08):86.
[19]陈天宇,吴凡,马世杰,等. 基于CS和LS-SVM的入侵检测算法[J].计算机技术与发展,2016,26(05):99.
　CHEN Tian-yu,WU Fan,MA Shi-jie,et al. Intrusion Detection Algorithm Based on Compressed Sensing and Least Square Support Vector Machine[J].,2016,26(08):99.
[20]曹耀彬,王亚刚. 免疫算法优化的RBF在入侵检测中的应用[J].计算机技术与发展,2017,27(06):114.
　CAO Yao-bin,WANG Ya-gang. Application of RBF Neural Network Optimized by Immune Algorithm in Intrusion Detection[J].,2017,27(08):114.

更新日期/Last Update: 2016-09-29