Statistical Analysis in Cyberspace

Data veracity, completeness, and clustering

Doctoral thesis (2021)

Authors

M.P. Roeling Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2021 M.P. Roeling
DOI
help_outline
https://doi.org/10.4233/uuid:f495fd3f-d131-40c9-a51e-e4a8bcc12c84
Cybersecurity Unsupervised learning Imputation Data veracity
More Info
expand_more

Published Date

2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

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.