[1]韩文智.计算机文本信息挖掘技术在网络安全中的应用[J].华侨大学学报(自然科学版),2016,37(1):67-70.[doi:10.11830/ISSN.1000-5013.2016.01.0067]
 HAN Wenzhi.Application of Computer Text Information Mining Technology in Network Security[J].Journal of Huaqiao University(Natural Science),2016,37(1):67-70.[doi:10.11830/ISSN.1000-5013.2016.01.0067]
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计算机文本信息挖掘技术在网络安全中的应用()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第37卷
期数:
2016年第1期
页码:
67-70
栏目:
出版日期:
2016-01-03

文章信息/Info

Title:
Application of Computer Text Information Mining Technology in Network Security
文章编号:
1000-5013(2016)01-0067-04
作者:
韩文智
四川职业技术学院 计算机科学系, 四川 遂宁 629000
Author(s):
HAN Wenzhi
Department of Computer Science, Sichuan Vocational and Technical College, Suining 629000, China
关键词:
文本信息 文本挖掘 文本分类 邻近分类
Keywords:
text information text mining text classification neighbor classification
分类号:
TP393
DOI:
10.11830/ISSN.1000-5013.2016.01.0067
文献标志码:
A
摘要:
针对网络文本信息的安全性判别问题,采取改进的邻近分类算法挖掘文本.该改进邻近分类方法在传统方法定义分类特征的同时,起用共线性判别矩阵,对具有共线属性的特征合并处理.这种改进策略,不仅可以增加分类特征的准确性,也可以加快文本信息的分类进程.对Spambase语料库开展实验研究,从精度、召回率、联判度、误差4个维度对分类效果进行评价.结果显示:改进的邻近分类方法具有明显的优势,可以更加准确地区分安全文本和危险文本.
Abstract:
In view of the security problem of network text information, we adopt an improved neighbor classification algorithm to carry out text mining. In improved nearest neighbor method, definition and classification are carried out by traditional method, and characteristics are merged by reinstating co-linear discriminant matrix of collinear attribute features. This improved strategy not only increase the accuracy of classification features, but also speed up the classification process of text information. An experimental study is carried out on the Spambase corpus, and the classification results are evaluated from 4 dimensions. Namely accuracy, recall rate, the degree of error, and the error. Results show that the improved method has obvious advantages, and that is more accurate in the area of security text and dangerous text.

参考文献/References:

[1] DAVIES S,MOORE A.Bayesian networks for lossless dataset compression[C]//Proceeding of International Conference Knowledge Discovery and Data Mining.San Diego:ACM Press,2013:387-391.
[2] 喻小光,陈维斌,陈荣鑫.一种数据规约的近似挖掘方法的实现[J].华侨大学学报(自然科学版),2008,29(3):370-374.
[3] MERETAKIS D,WUTHRICH B.Extending na?ve bayes classifiers using long item sets[C]//Proceeding of International Conference Knowledge Discovery and Data Mining.San Diego:ACM Press,2013:165-174.
[4] ESPOSITO F,MALERBA D,SEMERARO G,et al.A comparative analysis of methods for pruning decision trees[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,19(5):476-491.
[5] LAM S L Y,LEE D L.Feature reduction for neural network based text categorization[C]//Digital Symposium Collection of 6th International Conference on Database System for Advanced Application.[S.l.]:IEEE Press,2015:1121-1130.
[6] CESTNIK B,BRATKO I.On estimating probabilities in tree pruning, machine learning: EWSL-91[C]//Kodratoff Lecture Notes in Artificial Intelligence.Berlin:Springer,2015:138-150.
[7] ANDROUTSOPOULOS G,PALIOURAS V,KARKALETSIS G,et al.Learning to filter spam e-mail: A comparison of a na?ve Bayesian and a memory based approach[C]//Proceedings of 4th European Conference on Principles and Practice of Knowledge Discovery in Databases.London:Jerry Press,2000:1-13.
[8] SUN Lihua,ZHANG Jidong,LI Jingmei.An improved knearest neighbor system and its application to text classification[J].Applied Science and Technology,2002,29(2):25-27.
[9] 寸待杰,刘韶涛.采用内容挖掘的缅甸文字相似性文档检索[J].华侨大学学报(自然科学版),2013,34(5):521-524.
[10] RASTOGI R,SHIM K.Public: A decision tree that integrates building and pruning[C]//Proceeding of 24th International Conference on Very Large Data Bases.New York:[s.n.],2014:404-415.

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备注/Memo

备注/Memo:
收稿日期: 2015-11-16
通信作者: 韩文智(1966-),男,副教授,主要从事网络安全、软件技术的研究.E-mail:1691289966@qq.com.
基金项目: 四川省自然科学基金重点资助项目(15ZA0349).
更新日期/Last Update: 2016-01-20