Activity-based temporal anomaly detection in enterprise-cyber security

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Title: Activity-based temporal anomaly detection in enterprise-cyber security
Authors: Whitehouse, M
Evangelou, M
Adams, N
Item Type: Conference Paper
Abstract: Statistical anomaly detection is emerging as an important complement to signature-based methods for enterprise network defence. In this paper, we isolate a persistent structure in two different enterprise network data sources. This structure provides the basis of a regression-based anomaly detection method. The procedure is demonstrated on a large public domain data set.
Issue Date: 17-Nov-2016
Date of Acceptance: 21-Jul-2016
URI: http://hdl.handle.net/10044/1/39983
DOI: https://dx.doi.org/10.1109/ISI.2016.7745483
Publisher: IEEE
Journal / Book Title: IEEE International Big Data Analytics for Cybersecurity computing (BDAC'16) Workshop, IEEE International Conference on Intelligence and Security Informatics
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 Big Data Analytics for Cybersecurity computing (BDAC'16) Workshop, IEEE International Conference on Intelligence and Security Informatics
Keywords: Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Netflow data
Authentication events
NETWORK
Publication Status: Published
Start Date: 2016-09-28
Finish Date: 2016-09-30
Conference Place: Tucson, Arizona, USA
Appears in Collections:Mathematics
Statistics
Faculty of Medicine
Faculty of Natural Sciences
Epidemiology, Public Health and Primary Care



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