Detecting Mobile Malware Associated With Global Pandemics

Alfredo J. Perez, Sherali Zeadally, David Kingsley Tan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

More than 6 billion smartphones available worldwide can enable governments and public health organizations to develop apps to manage global pandemics. However, hackers can take advantage of this opportunity to target the public in nefarious ways through malware disguised as pandemics-related apps. A recent analysis conducted during the COVID-19 pandemic showed that several variants of COVID-19 related malware were installed by the public from nontrusted sources. We propose the use of app permissions and an extra feature (the total number of permissions) to develop a static detector using machine learning (ML) models to enable the fast-detection of pandemics-related Android malware at installation time. Using a dataset of more than 2000 COVID-19 related apps and by evaluating ML models created using decision trees and Naive Bayes, our results show that pandemics-related malware apps can be detected with an accuracy above 90% using decision tree models with app permissions and the proposed feature.

Original languageEnglish
Pages (from-to)45-54
Number of pages10
JournalIEEE Pervasive Computing
Volume22
Issue number4
DOIs
StatePublished - Oct 1 2023

Bibliographical note

Publisher Copyright:
© 2002-2012 IEEE.

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'Detecting Mobile Malware Associated With Global Pandemics'. Together they form a unique fingerprint.

Cite this