On Bayesian new edge prediction and anomaly detection in computer networks
File(s)
Accepted version
Author(s)
Metelli, Silvia
Heard, Nicholas
Type
Journal Article
Abstract
Monitoring computer network traffic for anomalous behaviour
presents an important security challenge. Arrivals of new edges in a
network graph represent connections between a client and server pair
not previously observed, and in rare cases these might suggest the
presence of intruders or malicious implants. We propose a Bayesian
model and anomaly detection method for simultaneously characterising existing network structure and modelling likely new edge formation. The method is demonstrated on real computer network authentication data and successfully identifies some machines which are
known to be compromised.
presents an important security challenge. Arrivals of new edges in a
network graph represent connections between a client and server pair
not previously observed, and in rare cases these might suggest the
presence of intruders or malicious implants. We propose a Bayesian
model and anomaly detection method for simultaneously characterising existing network structure and modelling likely new edge formation. The method is demonstrated on real computer network authentication data and successfully identifies some machines which are
known to be compromised.
Date Issued
2019-11-28
Date Acceptance
2019-07-12
Citation
Annals of Applied Statistics, 2019, 13 (4), pp.2586-2610
ISSN
1932-6157
Publisher
Institute of Mathematical Statistics
Start Page
2586
End Page
2610
Journal / Book Title
Annals of Applied Statistics
Volume
13
Issue
4
Copyright Statement
© Institute of Mathematical Statistics, 2019
Identifier
https://projecteuclid.org/euclid.aoas/1574910056
Subjects
0104 Statistics
Statistics & Probability
Publication Status
Published
Date Publish Online
2019-11-28