A Systematic Literature Review on the Mobile Malware Detection Methods

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

doi:10.1007/978-981-16-9576-6_19

A Systematic Literature Review on the Mobile Malware Detection Methods

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

School of Cybersecurity

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers
Editors	Ilsun You, Hwankuk Kim, Taek-Young Youn, Francesco Palmieri, Igor Kotenko
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	263-288
Number of pages	26
ISBN (Print)	9789811695759
DOIs	https://doi.org/10.1007/978-981-16-9576-6_19
Publication status	Published - 2022
Event	5th International Symposium on Mobile Internet Security, MobiSec 2021 - Jeju Island, Korea, Republic of Duration: 2021 Oct 7 → 2021 Oct 9

Publication series

Name	Communications in Computer and Information Science
Volume	1544 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	5th International Symposium on Mobile Internet Security, MobiSec 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/7 → 21/10/9

Bibliographical note

Funding Information:
Supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD190016ED), and a grant of the Korean Heath Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI19C0866).

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

Keywords

Dataset properties
Machine learning
Mobile detection
Mobile malware

ASJC Scopus subject areas

Computer Science(all)
Mathematics(all)

Access to Document

10.1007/978-981-16-9576-6_19

Cite this

Kim, Y. K., Lee, J. J., Go, M. H., Kang, H. Y., & Lee, K. (2022). A Systematic Literature Review on the Mobile Malware Detection Methods. In I. You, H. Kim, T-Y. Youn, F. Palmieri, & I. Kotenko (Eds.), Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers (pp. 263-288). (Communications in Computer and Information Science; Vol. 1544 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-9576-6_19

A Systematic Literature Review on the Mobile Malware Detection Methods. / Kim, Yu kyung; Lee, Jemin Justin; Go, Myong Hyun et al.
Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers. ed. / Ilsun You; Hwankuk Kim; Taek-Young Youn; Francesco Palmieri; Igor Kotenko. Springer Science and Business Media Deutschland GmbH, 2022. p. 263-288 (Communications in Computer and Information Science; Vol. 1544 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, YK, Lee, JJ, Go, MH, Kang, HY & Lee, K 2022, A Systematic Literature Review on the Mobile Malware Detection Methods. in I You, H Kim, T-Y Youn, F Palmieri & I Kotenko (eds), Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1544 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 263-288, 5th International Symposium on Mobile Internet Security, MobiSec 2021, Jeju Island, Korea, Republic of, 21/10/7. https://doi.org/10.1007/978-981-16-9576-6_19

Kim YK, Lee JJ, Go MH, Kang HY, Lee K. A Systematic Literature Review on the Mobile Malware Detection Methods. In You I, Kim H, Youn T-Y, Palmieri F, Kotenko I, editors, Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 263-288. (Communications in Computer and Information Science). doi: 10.1007/978-981-16-9576-6_19

Kim, Yu kyung ; Lee, Jemin Justin ; Go, Myong Hyun et al. / A Systematic Literature Review on the Mobile Malware Detection Methods. Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers. editor / Ilsun You ; Hwankuk Kim ; Taek-Young Youn ; Francesco Palmieri ; Igor Kotenko. Springer Science and Business Media Deutschland GmbH, 2022. pp. 263-288 (Communications in Computer and Information Science).

@inproceedings{251b547419634e04bfc5114561dedb52,

title = "A Systematic Literature Review on the Mobile Malware Detection Methods",

abstract = "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.",

keywords = "Dataset properties, Machine learning, Mobile detection, Mobile malware",

author = "Kim, {Yu kyung} and Lee, {Jemin Justin} and Go, {Myong Hyun} and Kang, {Hae Young} and Kyungho Lee",

note = "Funding Information: Supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD190016ED), and a grant of the Korean Heath Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI19C0866). Publisher Copyright: {\textcopyright} 2022, Springer Nature Singapore Pte Ltd.; 5th International Symposium on Mobile Internet Security, MobiSec 2021 ; Conference date: 07-10-2021 Through 09-10-2021",

year = "2022",

doi = "10.1007/978-981-16-9576-6_19",

language = "English",

isbn = "9789811695759",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "263--288",

editor = "Ilsun You and Hwankuk Kim and Taek-Young Youn and Francesco Palmieri and Igor Kotenko",

booktitle = "Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers",

address = "Germany",

}

TY - GEN

T1 - A Systematic Literature Review on the Mobile Malware Detection Methods

AU - Kim, Yu kyung

AU - Lee, Jemin Justin

AU - Go, Myong Hyun

AU - Kang, Hae Young

AU - Lee, Kyungho

N1 - Funding Information: Supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD190016ED), and a grant of the Korean Heath Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI19C0866). Publisher Copyright: © 2022, Springer Nature Singapore Pte Ltd.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Dataset properties

KW - Machine learning

KW - Mobile detection

KW - Mobile malware

UR - http://www.scopus.com/inward/record.url?scp=85124648872&partnerID=8YFLogxK

U2 - 10.1007/978-981-16-9576-6_19

DO - 10.1007/978-981-16-9576-6_19

M3 - Conference contribution

AN - SCOPUS:85124648872

SN - 9789811695759

T3 - Communications in Computer and Information Science

SP - 263

EP - 288

BT - Mobile Internet Security - 5th International Symposium, MobiSec 2021, Revised Selected Papers

A2 - You, Ilsun

A2 - Kim, Hwankuk

A2 - Youn, Taek-Young

A2 - Palmieri, Francesco

A2 - Kotenko, Igor

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Symposium on Mobile Internet Security, MobiSec 2021

Y2 - 7 October 2021 through 9 October 2021

ER -