Statistical Analysis in Cyberspace: Data veracity, completeness, and clustering

M.P. Roeling

doi:10.4233/uuid:f495fd3f-d131-40c9-a51e-e4a8bcc12c84

Statistical Analysis in Cyberspace: Data veracity, completeness, and clustering

M.P. Roeling

Cyber Security

Research output: Thesis › Dissertation (TU Delft)

725 Downloads (Pure)

Abstract

This thesis presents several methodological and statistical solutions to problems encountered in cyber security. We investigated the effects of compromised data veracity in state estimators and fraud detection systems, a model to impute missing data in attributes of linked observations, and an unsupervised approach to detect infected machines in a computer network.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	Delft University of Technology
Supervisors/Advisors	Verwer, S.E., Supervisor van den Berg, J., Supervisor Lagendijk, R.L., Supervisor
Thesis sponsors	Engineering and Physical Sciences Research Council (EPSRC)
Award date	7 Jun 2021
Place of Publication	Gouda
Print ISBNs	978-94-6423-299-8
DOIs	https://doi.org/10.4233/uuid:f495fd3f-d131-40c9-a51e-e4a8bcc12c84
Publication status	Published - 2021

Keywords

Cybersecurity
Unsupervised learning
Imputation
Data veracity

Access to Document

10.4233/uuid:f495fd3f-d131-40c9-a51e-e4a8bcc12c84

PhDthesis_MarkPatrickRoeling_completeFinal published version, 17.2 MB

Cite this

@phdthesis{f495fd3fd13140c9a51ee4a8bcc12c84,

title = "Statistical Analysis in Cyberspace: Data veracity, completeness, and clustering",

abstract = "This thesis presents several methodological and statistical solutions to problems encountered in cyber security. We investigated the effects of compromised data veracity in state estimators and fraud detection systems, a model to impute missing data in attributes of linked observations, and an unsupervised approach to detect infected machines in a computer network.",

keywords = "Cybersecurity, Unsupervised learning, Imputation, Data veracity",

author = "M.P. Roeling",

year = "2021",

doi = "10.4233/uuid:f495fd3f-d131-40c9-a51e-e4a8bcc12c84",

language = "English",

isbn = "978-94-6423-299-8",

type = "Dissertation (TU Delft)",

school = "Delft University of Technology",

}

TY - THES

T1 - Statistical Analysis in Cyberspace

T2 - Data veracity, completeness, and clustering

AU - Roeling, M.P.

PY - 2021

Y1 - 2021

N2 - This thesis presents several methodological and statistical solutions to problems encountered in cyber security. We investigated the effects of compromised data veracity in state estimators and fraud detection systems, a model to impute missing data in attributes of linked observations, and an unsupervised approach to detect infected machines in a computer network.

AB - This thesis presents several methodological and statistical solutions to problems encountered in cyber security. We investigated the effects of compromised data veracity in state estimators and fraud detection systems, a model to impute missing data in attributes of linked observations, and an unsupervised approach to detect infected machines in a computer network.

KW - Cybersecurity

KW - Unsupervised learning

KW - Imputation

KW - Data veracity

U2 - 10.4233/uuid:f495fd3f-d131-40c9-a51e-e4a8bcc12c84

DO - 10.4233/uuid:f495fd3f-d131-40c9-a51e-e4a8bcc12c84

M3 - Dissertation (TU Delft)

SN - 978-94-6423-299-8

CY - Gouda

ER -

Statistical Analysis in Cyberspace: Data veracity, completeness, and clustering

Abstract

Keywords

Access to Document

Fingerprint

Cite this