NDLI: Unveiling core network-wide communication patterns through application traffic activity graph decomposition

Content Provider	ACM Digital Library
Author	Sharafuddin, Esam Zhang, Zhi-Li Jin, Yu
Abstract	As Internet communications and applications become more complex,operating, managing and securing networks have become increasingly challenging tasks. There are urgent demands for more sophisticated techniques for understanding and analyzing the behavioral characteristics of network traffic. In this paper, we study the network traffic behaviors using traffic activity graphs (TAGs), which capture the interactions among hosts engaging in certain types of communications and their collective behavior. TAGs derived from real network traffic are large, sparse, yet seemingly complex and richly connected, therefore difficult to visualize and comprehend. In order to analyze and characterize these TAGs, we propose a novel statistical traffic graph decomposition technique based on orthogonal nonnegative matrix tri-factorization (tNMF) to decompose and extract the core host interaction patterns and other structural properties. Using the real network traffic traces, we demonstrate that our tNMF-based graph decomposition technique produces meaningful and interpretable results. It enables us to characterize and quantify the key structural properties of large and sparse TAGs associated with various applications, and study their formation and evolution.
Starting Page	49
Ending Page	60
Page Count	12
File Format	PDF
ISSN	01635999
DOI	10.1145/2492101.1555356
Journal	ACM SIGMETRICS Performance Evaluation Review (PERV)
Volume Number	37
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-01-10
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Graph decomposition Traffic graph Application classification Nonnegative matrix factorization Anomaly detection
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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Unveiling core network-wide communication patterns through application traffic activity graph decomposition

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