|Table of Contents|

Intrusion detection algorithm based on rough weightily averaged one-dependence estimators(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年04期
Page:
420-
Research Field:
Publishing date:

Info

Title:
Intrusion detection algorithm based on rough weightily averaged one-dependence estimators
Author(s):
Geng Xiachen1Li Qianmu1Ye Dezhong1Wu Zhongzheng2Jiang Yong2
1.School of Computer Science and Engineering,Nanjing University of Science andTechnology,Nanjing 210094,China; 2.Nanjing Research and Development Center,Zhongxing Telecommunication Equipment Corporation,Nanjing 320100,China
Keywords:
intrusion detection rough set theory attribute reduction Bayesian theory rough weightily averaged one-dependence estimators
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.004
Abstract:
Intrusion detection,as an important direction of network security,is gaining more and more attentions.A large number of traditional data mining algorithms are applied to the data analysis field of intrusion detection.With the increasing of network bandwidth,the great increasing amount of data and the various kinds of protocol types make the applications of these traditional algorithms encounter many reality problems,such as poor accuracy,low operating efficiency,difficulties of parameter selection,etc.In this paper,we propose an intrusion detection algorithm called rough weightily averaged one-dependence estimator,which is based on the rough set theory and Bayesian theory.This algorithm uses a subtraction method based on the rough set theory to reduce the attributes of network data,and uses weightily averaged one-dependence estimators to classify the data.By combining these two methods,this algorithm can do intrusion detection with low resource consumption and easy implementation.Experiment shows that the algorithm has better operating efficiency and accuracy compared with traditional algorithms.

References:

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Last Update: 2017-08-31