Paper Info
Title
Network Anomaly Detection Based on Dynamic Hierarchical Clustering of Cross Domain Data
Abstract
Cross domain data such as numerical or categorical types are ubiquitous in practical network. Network anomaly detection based on cluster analysis exist some difficulties, for example, the initial center of cluster analysis is sensitive and easy to fall into the local optimal solution. Cross domain data involved great information, but can't be effectively used, which will influence the performance of detection. In this paper, we proposed the dynamic hierarchical clustering method. Firstly, the feature selection based on information gain was used to reduce the feature dimension. Next, to measure the cross domain data, we defined the generalized Euclidean distance to extend the traditional Euclidean distance. Thirdly, dynamic clustering accuracy was set to guide the dynamic hierarchical clustering, guaranteeing the clustering accuracy as well as the convergence of clustering. Finally, Anomaly detection and classification model was constructed by using the training sample data. Simulation results show that the proposed algorithm can achieve higher detection rate and lower false detection rate.
Year
DOI
Venue
2017
10.1109/QRS-C.2017.39
2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)
Keywords
Field
DocType
network anomaly detection,feature selection,generalized Euclidean distance,clustering accuracy,dynamic hierarchical clustering
Hierarchical clustering,Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Computer science,Constrained clustering,Artificial intelligence,Cluster analysis
Conference
ISBN
Citations 
PageRank 
978-1-5386-2073-1
1
0.38
References 
Authors
3
9
Name
Order
Citations
PageRank
Liu Yang120033.54
Hongping Xu210.38
Hang Yi311.06
Zhen Lin4304.53
jian kang5266.86
Weiqiang Xia611.06
Qingping Shi711.06
Youping Liao810.72
Yulong Ying921.08