关键词: 非均衡数据, 重抽样方法, 网络入侵检测系统, NCL算法, AdaBoost算法

Abstract: Resampling methods are commonly used for dealing with the class-imbalance problem.To improve the detection rate of the minority attack in network intrusion detection and reduce the imbalance ratio.A novel algorithm named Fast Hierarchical Nearest Neighbor（FHNN） is prsented to select representative samples from network data sets.Taking the two-stage strategy with load balancing model for high-speed network intrusion detection system（HNIDS），the training dataset is splited by the protocol and the patterns for each dataset is built.Experimental results show FHNN is very efficient in tacking noise and majority class examples and faster than other methods while taking a linear order.FHNN is efficient method for rapid detection various types of attacks from the mass data.

Key words: imbalanced data, resampling methods, Network Intrusion Detection system, Neighborhood Cleaning Rule, Adaboost algorithm