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Secure LoRaWAN Root Key Update Scheme for IoT Environment Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

A Systematic Literature Review on the Mobile Malware Detection Methods

verfasst von : Yu-kyung Kim, Jemin Justin Lee, Myong-Hyun Go, Hae Young Kang, Kyungho Lee

Erschienen in: Mobile Internet Security

Verlag: Springer Nature Singapore

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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.

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Vorheriges Kapitel Detection of Business Email Compromise Attacks with Writing Style Analysis
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Metadaten
Titel
A Systematic Literature Review on the Mobile Malware Detection Methods
verfasst von
Yu-kyung Kim
Jemin Justin Lee
Myong-Hyun Go
Hae Young Kang
Kyungho Lee
Copyright-Jahr
2022
Verlag
Springer Nature Singapore
Buch
Mobile Internet Security

Print ISBN: 978-981-16-9575-9

Electronic ISBN: 978-981-16-9576-6

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-981-16-9576-6_19

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