[1]贾慧敏. 基于ML改进技术的IDS的设计与实现[J].计算机技术与发展,2015,25(06):114-118.
 JIA Hui-min. Design and Implementation of IDS Based on ML’ s Improved Technology[J].,2015,25(06):114-118.
点击复制

 基于ML改进技术的IDS的设计与实现()
分享到:

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

卷:
25
期数:
2015年06期
页码:
114-118
栏目:
安全与防范
出版日期:
2015-06-10

文章信息/Info

Title:
 Design and Implementation of IDS Based on ML’ s Improved Technology
文章编号:
1673-629X(2015)06-0114-05
作者:
贾慧敏
 大连交通大学
Author(s):
 JIA Hui-min
关键词:
 机器学习入侵检测系统网络入侵检测支持向量机
Keywords:
 MLIDSnetwork intrusion detectionSVM
分类号:
TP309.2
文献标志码:
A
摘要:
 网络入侵检测系统( IDS)是放置在比较重要的网段内或主机上,不停地监视各种传输数据包以及系统审计日志,进行智能分析与判断目的性攻击的系统,是当前网络安全研究的热点问题之一。文中将机器学习( ML)技术加入IDS的检测之中,不仅可以建立已知攻击的特征轮廓,还能检测出其变体和未知攻击,是对入侵检测技术的一个扩展。同时以Snif-fer捕获数据为基础数据包,设计并实现了一个基于改进支持向量机( SVM)核函数技术的IDS。通过实验数据对比,说明了该系统在日志分析以及网络嗅探方面的有效性,以及其在时间复杂度等方面的高效性。
Abstract:
 IDS is the system,that is placed on the more important subnets or hosts,constantly monitoring various data packets transmission and system audit logs,is one of the hot issues in the current network security research. In this paper,mix ML technology into IDS’ s de-tection,not only can create feature profile of known attacks,but also detect variants and unknown attacks,which is the extension for intru-sion detection technology. Also use Sniffer to capture data,designing and implementing an IDS based on an improved SVM’ s kernel function technology. By the experimental data comparison,illustrate the effectiveness on log analysis and network sniffer,and its high effi-ciency on time complexity.

相似文献/References:

[1]陈全 赵文辉 李洁 江雨燕.选择性集成学习算法的研究[J].计算机技术与发展,2010,(02):87.
 CHEN Quan,ZHAO Wen-hui,LI Jie,et al.Research of Selective Ensemble Learning Algorithm[J].,2010,(06):87.
[2]黄秀丽 王蔚.SVM在非平衡数据集中的应用[J].计算机技术与发展,2009,(06):190.
 HUANG Xiu-li,WANG Wei.Application of SVM in Imbalances Dataset[J].,2009,(06):190.
[3]鲁晓南 接标.一种基于个性化邮件特征的反垃圾邮件系统[J].计算机技术与发展,2009,(08):155.
 LU Xiao-nan,JIE Biao.An Individual Anti- Spam Technology[J].,2009,(06):155.
[4]张苗 张德贤.多类支持向量机文本分类方法[J].计算机技术与发展,2008,(03):139.
 ZHANG Miao,ZHANG De-xian.Research on Text Categorization Based on. M- SVMs[J].,2008,(06):139.
[5]汤萍萍 王红兵.基于强化学习的Web服务组合[J].计算机技术与发展,2008,(03):142.
 TANG Ping-ping,WANG Hong-bing.Web Service Composition Based on Reinforcement -Learning[J].,2008,(06):142.
[6]杨雪洁 赵姝 张燕平.基于商空间理论的冬小麦产量预测和分析[J].计算机技术与发展,2008,(03):249.
 YANG Xue-jie,ZHAO Shu,ZHANG Yan-ping.Analysis on Winter Wheat Yield Based on Quotient Space Theory[J].,2008,(06):249.
[7]汤伟 程家兴 纪霞.一种基于概率推理的邮件过滤系统的研究与设计[J].计算机技术与发展,2008,(08):76.
 TANG Wei,CHENG Jia-xing,JI Xia.Research and Design of a Spam Filtering System Based on Probability Inference[J].,2008,(06):76.
[8]孙海虹 丁华福.基于模糊粗糙集的Web文本分类[J].计算机技术与发展,2010,(07):21.
 SUN Hai-hong,DING Hua-fu.Web Document Classification Based on Fuzzy-Rough Set[J].,2010,(06):21.
[9]汤伟 程家兴 纪霞.统计学理论在邮件分类中的应用研究[J].计算机技术与发展,2008,(12):231.
 TANG Wei,CHENG Jia-xing,JI Xia.Research and Design of a Spam Filtering System Based on Statistical Learning Theory[J].,2008,(06):231.
[10]张高胤 谭成翔 汪海航.基于K-近邻算法的网页自动分类系统的研究及实现[J].计算机技术与发展,2007,(01):21.
 ZHANG Gao-yin,TAN Cheng-xiang,WANG Hai-hang.Design and Implementation of Web Page Automation Classification System Based on K- Nearest Neighbor Algorithm[J].,2007,(06):21.
[11]许肖,顾磊. 复杂背景下文本检测研究[J].计算机技术与发展,2015,25(03):40.
 XU Xiao,GU Lei. Research on Text Detection under Complex Background[J].,2015,25(06):40.
[12]张凯,齐丽娜. 基于连续隐马尔可夫模型的协作频谱检测[J].计算机技术与发展,2015,25(06):64.
 ZHANG Kai,QI Li-na. Cooperative Spectrum Detection Based on CHMM[J].,2015,25(06):64.
[13]闻彬,饶彬,赵君喆,等. 融合直推式学习和语义理解的词语倾向性识别[J].计算机技术与发展,2016,26(01):74.
 WEN Bin,RAO Bin,ZHAO Jun-zhe,et al. Identifying of Word Sentiment Orientation of Transductive Learning and Semantic Comprehension[J].,2016,26(06):74.
[14]白振凯,黄孝喜,王荣波,等. 基于主题模型的汉语动词隐喻识别[J].计算机技术与发展,2016,26(11):67.
 BAI Zhen-kai,HUANG Xiao-xi,WANG Rong-bo,et al. Chinese Verb Metaphor Recognition Based on Topic Model[J].,2016,26(06):67.
[15]吴聪,殷浩,黄中勇,等. 基于人工神经网络的车牌识别[J].计算机技术与发展,2016,26(12):160.
 WU Cong,YIN Hao,HUANG Zhong-yong,et al. Vehicle Plate Recognition Based on Artificial Neural Network[J].,2016,26(06):160.
[16]葛夕武[],朱超[],马骏毅[],等. 基于耦合隐马尔可夫模型的输电线路状态评估[J].计算机技术与发展,2017,27(04):154.
 GE Xi-wu[],ZHU Chao[],MA Jun-yi[],et al. State Evaluation of Transmission Line Based on CoupledHidden Markov Model[J].,2017,27(06):154.
[17]余敖,陈亮,彭敬涛. 基于迟滞ELM模型的短期风速预测[J].计算机技术与发展,2017,27(06):130.
 YU Ao,CHEN Liang,PENG Jing-tao. Short-term Wind Speed Forecasting by Using Hysteretic ELM Model[J].,2017,27(06):130.
[18]顾晓瑜,杨悦. 一种基于SVM的声源定位算法[J].计算机技术与发展,2017,27(09):70.
 GU Xiao-yu,YANG Yue. A Sound Source Localization Algorithm with Support Vector Machine[J].,2017,27(06):70.
[19]唐新晨. 基于认知计算的就业咨询智慧服务系统[J].计算机技术与发展,2017,27(11):166.
 TANG Xin-chen. Employment Consultation Intelligent Service System Based on Cognitive Computation[J].,2017,27(06):166.

更新日期/Last Update: 2015-08-05