摘要
本文提出了利用基于参考点的展开策略来改进现有的密度空间聚类算法,并利用改进后的聚类算法检测网络恶意数据流。为了验证其有效性,将该算法与K-Means进行对比,考察本文策略在聚类纯度、兰德指数和F值三种指标下的表现。实验结果表明,与K-Means算法相比,本文策略具有较高的聚类准确性(即纯度、兰德指数和F值较高)。
This paper proposes an effective network malicious flow detection algorithm,which is based on the improved density clustering algorithm by applying the reference point strategy and using the improved clustering to detect malicious flow.The improved algorithm in this paper uses reference points to cluster malicious flow,and considers different attack stages.In order to verify its effectiveness,the proposed algorithm was compared with K-Means in terms of three indicators,i.e,-clustering purity,rand index and F-measure.The experimental results show that compared with k-means algorithm,this strategy has higher clustering accuracy(i.e.higher purity,rand index and F measure).
作者
李卫华
LI Wei-hua(Department of Information Engineering,Longyan University,Longyan 364012,China)
出处
《遵义师范学院学报》
2019年第2期102-105,共4页
Journal of Zunyi Normal University
关键词
机器学习
改进聚类算法
恶意流检测
machine learning
improved clustering algorithm
malicious flow detection
