Disassortativity of computer networks

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Title: Disassortativity of computer networks
Authors: Rubin-Delanchy, P
Item Type: Conference Paper
Abstract: Network data is ubiquitous in cyber-security applications. Accurately modelling such data allows discovery of anomalous edges, subgraphs or paths, and is key to many signature-free cyber-security analytics. We present a recurring property of graphs originating from cyber-security applications, often considered a ‘corner case’ in the main literature on network data analysis, that greatly affects the performance of standard ‘off-the-shelf’ techniques. This is the property that similarity, in terms of network behaviour, does not imply connectivity, and in fact the reverse is often true. We call this disassortivity. The phenomenon is illustrated using network flow data collected on an enterprise network, and we show how Big Data analytics designed to detect unusual connectivity patterns can be improved.
Issue Date: 17-Nov-2016
Date of Acceptance: 18-Aug-2016
URI: http://hdl.handle.net/10044/1/42764
DOI: https://dx.doi.org/10.1109/ISI.2016.7745482
ISBN: 978-1-5090-3865-7
Publisher: IEEE
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Conference Name: IEEE International Conference on Intelligence and Security Informatics
Publication Status: Published
Start Date: 2016-09-28
Finish Date: 2016-09-30
Conference Place: Arizona, USA
Appears in Collections:Mathematics
Faculty of Natural Sciences

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