A triangle area based nearest neighbors approach to intrusion detection

NCU Institutional Repository > 管理學院 > 資訊管理學系 > 期刊論文 > Item 987654321/51795

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/51795

题名:	A triangle area based nearest neighbors approach to intrusion detection
作者:	Tsai,CF;Lin,CY
贡献者:	資訊管理學系
日期:	2010
上传时间:	2012-03-27 19:06:22 (UTC+8)
出版者:	國立中央大學
摘要:	Intrusion detection is a necessary step to identify unusual access or attacks to secure internal networks. In general, intrusion detection can be approached by machine learning techniques. In literature, advanced techniques by hybrid learning or ensemble methods have been considered, and related work has shown that they are superior to the models using single machine learning techniques. This paper proposes a hybrid learning model based on the triangle area based nearest neighbors (TANN) in order to detect attacks more effectively. In TANN, the k-means clustering is firstly used to obtain cluster centers corresponding to the attack classes, respectively. Then, the triangle area by two cluster centers with one data from the given dataset is calculated and formed a new feature signature of the data. Finally, the k-NN classifier is used to classify similar attacks based on the new feature represented by triangle areas. By using KDD-Cup '99 as the simulation dataset, the experimental results show that TANN can effectively detect intrusion attacks and provide higher accuracy and detection rates, and the lower false alarm rate than three baseline models based on support vector machines, k-NN, and the hybrid centroid-based classification model by combining k-means and k-NN. (C) 2009 Elsevier Ltd. All rights reserved.
關聯:	PATTERN RECOGNITION
显示于类别:	[資訊管理學系] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	560	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....