Can machine learning be used to detect malware?

Android OS case study

Authors

DOI:

https://doi.org/10.56394/aris2.v2i2.19

Keywords:

Android, Malware Detection, Machine Learning;, Vulnerabilities

Abstract

Nowadays everyone has one or even more than one smartphone or tablet. The existing applications with the most diverse purposes allow us to perform a series of tasks such as using home banking or checking the email, using only our smartphone/tablet. Android OS being one of the most used in this type of equipment becomes an appealing target for viruses, malware and others. At a time when technology is evolving faster and faster, both in terms of hardware and software, Artificial Intelligence has more and more weight in technological evolution, being used in the most diverse purposes. This review aims to demonstrate how Machine Learning can assist in identifying vulnerabilities in Android OS.

References

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J. Senanayake, H. Kalutarage, and M. O. Al-Kadri, "Android Mobile Malware Detection Using Machine Learning: A Systematic Review," Electronics, vol. 10, no. 13, p. 1606, 2021. [Online]. Available: https://www.mdpi.com/2079-9292/10/13/1606. DOI: https://doi.org/10.3390/electronics10131606

M. J. Page et al., "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews," BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71. DOI: https://doi.org/10.1136/bmj.n71

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Published

2022-12-30

How to Cite

[1]
A. Lima, “Can machine learning be used to detect malware? : Android OS case study ”, ARIS2-Journal, vol. 2, no. 2, pp. 24–30, Dec. 2022.