计算机工程与应用2013，Vol.49Issue(2)：89-91,108,4.DOI:10.3778/j.issn.1002-8331.1207-0359
基于改进LDA和CNN的网络入侵聚类
Network intrusion clustering method based on improved LDA and CNN
摘要
Abstract
A hybrid method of improved Linear Discriminant Analysis (LDA) and Center-based Nearest Neighbor(CNN) classifier for clustering of network intrusions is proposed. The improved LDA is employed to reduce the dimensions of sample vector, and then the center-based nearest neighbor classifier is used to cluster for the data of network intrusions. The proposed algorithm not only reduces the clustering time of the algorithm, but also improves the clustering ability. Experimental results indicate that the proposed algorithm obtains higher clustering capability contrast to other models at a higher detection rate and a lower false alarm rate.关键词
线性判别分析/中心近邻法/网络入侵/聚类/降维Key words
linear discriminant analysis/ center-based nearest neighbor/ network intrusions/ clustering/ dimension reduction分类
信息技术与安全科学引用本文复制引用
谭立志,李二喜,欧阳艾嘉,贺明华,周旭..基于改进LDA和CNN的网络入侵聚类[J].计算机工程与应用,2013,49(2):89-91,108,4.基金项目
国家自然科学基金(No.61202109) （No.61202109）
井冈山大学科研基金项目(No.JR1216). （No.JR1216）
