计算机工程与应用2012，Vol.48Issue(2)：86-89,4.DOI:10.3778/j.issn.1002-8331.2012.02.024
自适应聚类算法在DDoS攻击检测中的应用
Application of adaptive clustering algorithm on DDoS attacks detection
李丽娟 1李少东2
作者信息
- 1. 湖南大学计算机与通信学院,长沙410082
- 2. 湖南大学 网络与信息安全湖南省重点实验室,长沙410082
- 折叠
摘要
Abstract
The k-means algorithm in DDoS attack detection is sensitive to the initial cluster centers and need to input the number of clusters. For the above two drawbacks, a new adaptive clustering algorithm based on dynamic index and the initial center selection is proposed, and use it to establish the DDoS attack detection model. Then the detection model is tested by using the LLS_DDoS_1.0 data sets, and is compared with the k-means algorithm. The result show that the method improves the detection rate and reduces the false alarm rate. So it is an effective detection method.关键词
DDoS攻击检测/k-means算法/动态指数/自适应聚类算法Key words
DDoS attacks detection/ k-means algorithm/ dynamic index/ adaptive clustering algorithm分类
信息技术与安全科学引用本文复制引用
李丽娟,李少东..自适应聚类算法在DDoS攻击检测中的应用[J].计算机工程与应用,2012,48(2):86-89,4.
