A Systematic Literature Review on the Mobile Malware Detection Methods

Yu kyung Kim, Jemin Justin Lee, Myong Hyun Go, Hae Young Kang, Kyungho Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


With the advent of the 5G network, the number of mobile users has drastically increased. Consequently, the users are much more susceptible to cyber-attacks such as mobile malware. In order to combat mobile malware, recent studies have employed machine learning techniques. This paper revisits existing research on machine learning-based mobile malware detection in cybersecurity. Our study focuses on subjects such as mobile system destruction and information leaks. We explore the mobile malware detection techniques utilized in recent studies based on the attack intentions such as (i) Server, (ii) Network, (iii) Client Software, (iv) Client Hardware, and (v) User. We hope our study can provide future research directions and a framework for a thorough evaluation. Furthermore, we review and summarize security challenges related to cybersecurity that can lead to improved and more practical research.

Original languageEnglish
Title of host publicationMobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers
EditorsIlsun You, Hwankuk Kim, Taek-Young Youn, Francesco Palmieri, Igor Kotenko
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages26
ISBN (Print)9789811695759
Publication statusPublished - 2022
Event5th International Symposium on Mobile Internet Security, MobiSec 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Oct 72021 Oct 9

Publication series

NameCommunications in Computer and Information Science
Volume1544 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference5th International Symposium on Mobile Internet Security, MobiSec 2021
Country/TerritoryKorea, Republic of
CityJeju Island

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Singapore Pte Ltd.


  • Dataset properties
  • Machine learning
  • Mobile detection
  • Mobile malware

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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