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University of Ontario Institute of Technology

Virtual machine detection through Central Processing Unit (CPU) detail anomalies

Abstract

dc:description.abstract

Malware analysts commonly use virtual machines to provide safe environments to study malware. Malware authors in response, include virtual machine detection functions in their malware so it changes its behavior should a virtual machine be detected. It is therefore important for researchers to continuously uncover new virtual machine detection methods that may be exploited by criminals. This thesis explores a method of virtual machine detection that looks for inconsistencies in the following Central Processing Unit (CPU) details: the CPU model, the number of physical cores, the number of logical cores and the cache capacities. Should inconsistencies be detected, a virtual machine is present. We explore our method in scenarios where all CPU cores are assigned to the test virtual machines to determine if inconsistencies exist. In our tests, many of the hypervisors tested possessed inconsistencies that could be used to deduce the presence of a virtual machine.

Degree

thesis:*
Name thesis:degree_name
Master of Science (MSc)
Discipline thesis:degree_discipline
Computer Science
Grantor
University of Ontario Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Mettrick, David
Advisor dc:contributor.advisor
  • Hung, Patrick

Subjects

dc:subject × 5

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10155/1585
OAI identifier oai:identifier
oai:ontariotechu.scholaris.ca:10155/1585

Chain of custody

source
Harvested from
Ontario Institute of Technology
Base URL
ontariotechu.scholaris.ca/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Mettrick, David. Virtual machine detection through Central Processing Unit (CPU) detail anomalies. University of Ontario Institute of Technology, 2022. https://hdl.handle.net/10155/1585