Title :
Hierarchical summarization techniques for network traffic
Author :
Mahmood, A.N. ; Leckie, C. ; Islam, R. ; Tari, Z.
Author_Institution :
Sch. of Comput. Sci. & I.T., R. Melbourne Inst. of Technol. Univ., Melbourne, VIC, Australia
Abstract :
In today´s high speed networks it is becoming increasingly challenging for network managers to understand the nature of the traffic that is carried in their network. A major problem for traffic analysis in this context is how to extract a concise yet accurate summary of the relevant aggregate traffic flows that are present in network traces. In this paper, we present two summarization techniques to minimize the size of the traffic flow report that is generated by a hierarchical cluster analysis tool. By analyzing the accuracy and compaction gain of our approach on a standard benchmark dataset, we demonstrate that our approach achieves more accurate summaries than those of an existing tool that is based on frequent itemset mining.
Keywords :
Internet; data mining; pattern clustering; telecommunication traffic; frequent itemset mining; hierarchical cluster analysis; hierarchical summarization techniques; high speed networks; network traffic; traffic analysis; traffic flows; Aggregates; Artificial intelligence; Clustering algorithms; Compaction; IP networks; Peer to peer computing; Protocols; Cluster analysis; Internet management; Summarization; Traffic analysis;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8754-7
Electronic_ISBN :
pending
DOI :
10.1109/ICIEA.2011.5976009