基于聚类的邻域检测器生成算法[EB/OL]
北京：中国科技论文在线
提出一种基于聚类的邻域检测器生成算法
对邻域否定选择算法和聚类技术进行深入研究
） Abstract： Neighborhood Negative Selection(NNS) algorithm needs to traverse the whole self-samples and leads to large amount of calculation , it is proved to be a very time consuming for this algorithm, at the same time there are phenomena about overlap rate higher at matching stage
To address this issue, making in-depth study on NNS and clustering method, it is proposed a novel Neighborhood Detector Algorithm Based on Clustering(NDAC)
The self-samples are mapped to neighborhood space and they are used to cluster, Meanwhile, random detectors are trained and become mature neighborhood detectors
The algorithm generates detectors by shortening the time and solving the high overlap problem
In KDD CUP 1999 data sets to evaluate the results of simulation show that, the algorithm can solve the above mentioned problems effectively and increase of efficiency
Keywords： intrusion detection system
免疫入侵检测 通信联系人： 【收录情况】 中国科技论文在线： 张凤斌
） 摘要： 邻域否定选择算法对每个自体样本遍历导致计算量大
