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DTSTART;TZID=America/Chicago:20181111T110000
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UID:submissions.supercomputing.org_SC18_sess159_ws_indis106@linklings.com
SUMMARY:Flowzilla: A Methodology for Detecting Data Transfer Anomalies in 
 Research Networks
DESCRIPTION:Anna Giannakou, Dan Gunter, and Sean Peisert (Lawrence Berkele
 y National Laboratory)\n\nResearch networks are designed to support high v
 olume scientific data transfers that span multiple network links. Like any
  other network, research networks experience anomalies. Anomalies are devi
 ations from profiles of normality in a science network’s traffic levels. D
 iagnosing anomalies is critical both for network operators and scientists.
  In this paper, we present Flowzilla, a general framework for detecting an
 d quantifying anomalies on scientific data transfers of arbitrary size. Fl
 owzilla incorporates Random Forest Regression for predicting the size of d
 ata transfers and utilizes an adaptive threshold mechanism for detecting o
 utliers. Our results demonstrate that our framework achieves up to 92.5% d
 etection accuracy. Furthermore, we are able to predict data transfer sizes
  up to 10 weeks after training with accuracy above 90%.\n\nTag: Architectu
 res, Networks, Security\n\nRegistration Category: Workshop Reg Pass\n\nSes
 sion Chairs: Ilya Baldin (Thomas Jefferson National Accelerator Facility);
  Paola Grosso (University of Amsterdam, Netherlands); Mary Hester (Dutch N
 ational Institute for Subatomic Physics); and Michelle Zhu (Montclair Stat
 e University)\n\n
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