@inproceedings{361a0f561cbf4c5fb06860be778eb28c,
title = "Link-level interpretation of eigenanalysis for network traffic flows",
abstract = "This paper presents a novel approach to interpret eigenanalysis of network statistics at the link level in order to identify traffic flows efficiently. It jointly uses and interprets eigencoefficients (frequency) and components of eigenvectors (time) to quantify their importance on each sample (each component of link traffic vector) in eigensubspace representation. We apply the proposed method to analyze the traffic data obtained from Internet2 network. Its merit and superiority over eigenflow based traditional analysis methods are displayed for a few network scenarios with anomalies. It is highlighted that the link-level resolution provided by the method offers advantages also for multi-layer traffic engineering, and it is currently being studied by the authors.",
keywords = "Eigen Analysis, Eigenvalue, Eigenvector, Network Traffic Engineering, Principal Component Analysis (PCA)",
author = "Irfan Lateef and Akansu, {Ali N.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 51st Annual Conference on Information Sciences and Systems, CISS 2017 ; Conference date: 22-03-2017 Through 24-03-2017",
year = "2017",
month = may,
day = "10",
doi = "10.1109/CISS.2017.7926117",
language = "English (US)",
series = "2017 51st Annual Conference on Information Sciences and Systems, CISS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 51st Annual Conference on Information Sciences and Systems, CISS 2017",
address = "United States",
}