Activity-based temporal anomaly detection in enterprise-cyber security
File(s)WHITEHOUSE.pdf (477.05 KB)
Accepted version
Author(s)
Whitehouse, M
Evangelou, M
Adams, N
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.
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.
Date Issued
2016-11-17
Date Acceptance
2016-07-21
Citation
IEEE International Big Data Analytics for Cybersecurity computing (BDAC'16) Workshop, IEEE International Conference on Intelligence and Security Informatics, 2016
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.
Source
IEEE International Big Data Analytics for Cybersecurity computing (BDAC'16) Workshop, IEEE International Conference on Intelligence and Security Informatics
Subjects
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
Coverage Spatial
Tucson, Arizona, USA