节点文献

Intrusion detection using rough set classification

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 张连华张冠华郁郎张洁白英彩

【Author】 ZHANG Lian-hua ZHANG Guan-hua, YU LangZHANG Jie , BAI Ying-cai(Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, China) (An Zong Information Technology Inc., Shanghai 200042, China)

【机构】 Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghai 200030China An Zong Information Technology Inc.Shanghai 200042Chinawww. antpower. orgDepartment of ComputingHong Kong Polytechnic UniversityHong KongChina

【摘要】 <正> Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of "IF-THEN" rules, which have the advantage of explication. Tests and compa

【Abstract】 Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of "IF-THEN" rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

【基金】 Project (No. 2001 AA40437.2) partially supported by the Hi-Tech Research,Development Program (863) of China
  • 【文献出处】 Journal of Zhejiang University Science ,浙江大学学报(英文版) , 编辑部邮箱 ,2004年09期
  • 【分类号】TP18
  • 【被引频次】2
  • 【下载频次】17
节点文献中: 

本文链接的文献网络图示:

本文的引文网络