A Comprehensive Analysis on Various Deep Learning Techniques for Malware Detection in Android Mobile Devices

被引：0

作者：

Anusha M. ^{[1
]}

Karthika M. ^{[1
]}

机构：

[1] PG and Research Department of Computer Science, National College (Affiliated to Bharathidasan University), Tiruchirappalli

来源：

SN Computer Science | / 4卷 / 5期

基金：

英国科研创新办公室;

关键词：

Access control; Android operating system; Malicious detection; Privacy; Security;

D O I：

10.1007/s42979-023-01894-y

中图分类号：

学科分类号：

摘要：

Due to the recent advancement in cellular communication and android operating system, most of the people prefer android mobile phones for their day-to-day activities. The main advantages of android smart device are its ease of use and efficient processing in terms of storage, computation and communication. However, android smart phones are frequently vulnerable to various types of malicious attack from various intruders. Due to the malicious attacks, the mobile devices are getting compromised by the third party applications and there is evident risk of privacy that intruders will gain access control over sensitive information from the compromised mobile devices. In order to overcome the malicious attacks on android operating mobile devices, various researchers has proposed various solutions on providing efficient malware detection system to secure android mobile devices. In this paper, a comprehensive survey on deep learning techniques based on various malware detection systems has been carried out in detail in order to highlight the advantages and limitations of the existing system. Moreover, the proposed survey provides detailed analysis which helps the future researchers to improve the malware detection system in the future. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

引用

共 44 条

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