网络流量特征选择方法中的分治投票策略研究
The Divide-Conquer and Voting Strategy for Traffic Feature Selection
查看参考文献14篇
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文摘
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特征选择作为机器学习过程中的预处理步骤,是影响分类性能的关键因素.网络流量具有数据量大,特征维度高的特点,如何快速提取特征子集,并提高分类效率对于基于机器学习的流量分类方法具有重要意义.本文提出基于分治与投票策略的特征提取方法,将数据集分裂为多个子集,分别执行特征提取算法,利用投票方法获得最后的特征子集.实验表明可有效提高特征提取的时间效率,同时使分类器取得良好的分类准确率. |
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其他语种文摘
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Feature selection as a substantial preprocess step is a key factor for improvement of classification accuracy.The network traffic is characterized by huge volume and high dimensions.So how to extract the optimal feature subset in short time is practical for traffic classification based on machine learning.A novel method is proposed,which partitions the traffic dataset into several small subsets,and applies special feature selection algorithm to them respectively.Finally,the optimal feature subset is obtained by voting on these alternative feature subsets.The experiment results show that the proposed method has good time efficiency in searching optimal features and helps to improve classification accuracy efficiently. |
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来源
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电子学报
,2015,43(4):795-799 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2015.04.024
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关键词
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分治
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投票
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流量分类
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特征选择
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地址
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1.
浙江大学计算机学院, 浙江, 杭州, 310027
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浙江大学图书与信息中心, 浙江, 杭州, 310027
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嘉兴职业技术学院, 浙江, 嘉兴, 314036
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家973计划
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浙江省重点科技创新团队
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国家自然科学基金
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国家科技支撑计划项目
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国家863计划
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文献收藏号
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CSCD:5442862
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参考文献 共
14
共1页
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|
1.
Dainotti A. Issues and future directions in traffic classification.
Network,IEEE,2012,26(1):35-40
|
CSCD被引
48
次
|
|
|
|
|
2.
Yaguan Q. Network traffic anomaly detection based on maximum entropy model.
Chinese Journal of Electronics,2012,21(3):579-582
|
CSCD被引
1
次
|
|
|
|
|
3.
Zhang H. Feature selection for optimizing traffic classification.
Computer Communications,2012,35(12):1457-1471
|
CSCD被引
19
次
|
|
|
|
|
4.
Moore A.
Discriminators for Use in Flow-Based Classification,2005
|
CSCD被引
13
次
|
|
|
|
|
5.
Jain A. Feature selection:Evaluation,application,and small sample performance.
IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(2):153-158
|
CSCD被引
62
次
|
|
|
|
|
6.
Hall M A.
Correlation-based feature selection for machine learning,1999
|
CSCD被引
22
次
|
|
|
|
|
7.
Rich E.
Artificial Intelligence,1991
|
CSCD被引
3
次
|
|
|
|
|
8.
Sun M. A new method of feature selection for flow classification.
Physics Procedia,2012,24:1729-1736
|
CSCD被引
1
次
|
|
|
|
|
9.
宁卓. 利用流量特征的GIDS报文分类优化算法.
电子学报,2012,40(3):530-537
|
CSCD被引
2
次
|
|
|
|
|
10.
Jamil H A. Analysis of features selection for P2P traffic detection using support vector machine.
Information and Communication Technology (ICoICT),2013:116-121
|
CSCD被引
1
次
|
|
|
|
|
11.
Alazab A. Using feature selection for intrusion detection system.
Communications and Information Technologies (ISCIT),2012:296-301
|
CSCD被引
1
次
|
|
|
|
|
12.
Moore A W. Internet traffic classification using bayesian analysis techniques.
ACM SIGMETRICS Performance Evaluation Review,2005:50-60
|
CSCD被引
3
次
|
|
|
|
|
13.
Zhang Y. A method for real-time peer-to-peer traffic classification based on C4.5.
2010 12th IEEE International Conference on Communication Technology (ICCT),2010:1192-1195
|
CSCD被引
1
次
|
|
|
|
|
14.
Moore Andrew W.
Dataset,2013
|
CSCD被引
1
次
|
|
|
|
|
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