Model-based clustering and new edge modelling in large computer networks

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Title: Model-based clustering and new edge modelling in large computer networks
Authors: Metelli
Heard
Item Type: Conference Paper
Abstract: Computer networks are complex and the analysis of their structure in search for anomalous behaviour is both a challenging and important task for cyber security. For instance, new edges, i.e. connections from a host or user to a computer that has not been connected to before, provide potentially strong statistical evidence for detecting anomalies. Unusual new edges can sometimes be indicative of both legitimate activity, such as automated update requests permitted by the client, and illegitimate activity, such as denial of service (DoS) attacks to cause service disruption or intruders escalating privileges by traversing through the host network. In both cases, capturing and accumulating evidence of anomalous new edge formation represents an important security application. Computer networks tend to exhibit an underlying cluster structure, where nodes are naturally grouped together based on similar connection patterns. What constitutes anomalous behaviour may strongly differ between clusters, so inferring these peer groups constitutes an important step in modelling the types of new connections a user would make. In this article, we present a two-step Bayesian statistical method aimed at clustering similar users inside the network and simultaneously modelling new edge activity, exploiting both overall-level and cluster-level covariates.
Issue Date: 17-Nov-2016
Date of Acceptance: 21-Jul-2016
URI: http://hdl.handle.net/10044/1/42762
DOI: https://dx.doi.org/10.1109/ISI.2016.7745449
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
Statistics
Faculty of Natural Sciences



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