|Table of Contents|

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


Research Field:
Publishing date:


Intrusion detection algorithm based on rough weightily averaged one-dependence estimators
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
intrusion detection rough set theory attribute reduction Bayesian theory rough weightily averaged one-dependence estimators
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.


[1] 杨雅辉,黄海珍,沈晴霓,等.基于增量式GHSOM神经网络模型的入侵检测研究[J].计算机学报,2014,37(5):1216-1224.Yang Yahui,Huang Haizhen,Shen Qingni,et al.Research on intrusion detection based on incremental GHSOM[J].Chinese Journal of Computers,2014,37(5):1216-1224.
[2]夏秦,王志文,卢柯.入侵检测系统利用信息熵检测网络攻击的方法[J].西安交通大学学报,2013,47(2):14-19.Xia Qin,Wang Zhiwen,Ke Lu.A method to detect network attacks using entropy in the intrusion detection system[J].Journal of Xi’an Jiaotong University,2013,47(2):14-19.
[3]Shakshuki E M,Kang N,Sheltami T R.EAACK—a secure intrusion-detection system for MANETs[J].IEEE Transactions on Industrial Electronics,2013,60(3):1089-1098.
[4]田志宏,王佰玲,张伟哲,等.基于上下文验证的网络入侵检测模型[J].计算机研究与发展,2013,50(3):498-508.Tian Zhihong,Wang Bailing,Zhang Weizhe,et al.Network intrusion detection model based on context verification[J].Journal of Computer Research & Development,2013,50(3):498-508.
[5]李国栋,胡建平,夏克文.基于云PSO的RVM入侵检测[J].控制与决策,2015,30(4):698-702.Li Guodong,Hu Jianping,Xia Kewen.Intrusion detection using relevance vector machine based on cloud particle swarm optimization[J].Control & Decision,2015,30(4):698-702.
[6]武小年,彭小金,杨宇洋,等.入侵检测中基于SVM的两级特征选择方法[J].通信学报,2015,36(4):19-26.Wu Xiaonian,Peng Xiaojin,Yang Yuyang,et al.Two-level feature selection method based on SVM for intrusion detection[J].Journal on Communications,2015,36(4):19-26.
[7]陈友,程学旗,李洋,等.基于特征选择的轻量级入侵检测系统[J].软件学报,2007,18(7):1639-1651. Chen You,Cheng Xueqi,Li Yang,et al.Lightweight intrusion detection system based on feature selection[J].Journal of Software,2007,18(7):1639-1651.
[8]Pawlak Z.Rough set theory and its applications to data analysis[J].Cybernetics & Systems,2010,29(29):661-688.
[9]Chen Hongmei,Li Tianrui,Luo Chuan,et al.A decision-theoretic rough set approach for dynamic data mining[J].IEEE Transactions on Fuzzy Systems,2015,23(6):1958-1970.
[10]Jaddi N S,Abdullah S.An interactive rough set attribute reduction using great deluge algorithm[C]//International Visual Informatics Conference.Selangor,Malaysia:Springer International Publishing,2013:285-299.
[11]Sarkar A M J,Lee Y K,Lee S.A smoothed naive Bayes-based classifier for activity recognition[J].Iete Technical Review,2014,27(2):107-119.
[12]Xie Z.A classifier selection strategy for lazy Bayesian rules based on local accuracy estimation[C]//Education Technology and Computer Science,ETCS’09,First International Workshop on.Wuhan,Hubei,China:IEEE,2009:156-159.
[13]Qiu Chen,Jiang Liangxiao,Li Chaoqun.Not always simple classification:learning super parent for class probability estimation[J].Expert Systems with Applications,2015,42(13):5433-5440.
[14]Jiang Liangxiao,Zhang Harry.Weightily averaged one-dependence estimators[C]//Pacific Rim International Conference on Artificial Intelligence.Cuilin,China:Springer-Verlag Berlin,Heidelberg,2006:970-974.
[15]DARPA intrusion detection evaluation[EB/OL].http://www.ll.mit.edu/ideval/data/1999data.html,2016-06-29.
[16]KDD-CUP-99 task description[EB/OL].http://kdd.ics.uci.edu/databases/kddcup99/task.html,2016-06-29.
[17]KDD cup 1999 data[EB/OL].http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html,2016-06-29.


Last Update: 2017-08-31