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.
|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|
|Number of pages||26|
|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
|Name||Communications in Computer and Information Science|
|Conference||5th International Symposium on Mobile Internet Security, MobiSec 2021|
|Country/Territory||Korea, Republic of|
|Period||21/10/7 → 21/10/9|
Bibliographical noteFunding 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).
© 2022, Springer Nature Singapore Pte Ltd.
- Dataset properties
- Machine learning
- Mobile detection
- Mobile malware
ASJC Scopus subject areas
- Computer Science(all)