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Lopes, J. P., Serrão, C., Nunes, L., De Almeida,  A. & Oliveira, J. (2019). Overview of machine learning methods for Android malware identification. In Varol, A., Karabatak, M., Varol, C. and Teke, S. (Ed.), 2019 7th International Symposium on Digital Forensics and Security (ISDFS). Barcelos: IEEE.

J. P. Lopes et al.,  "Overview of machine learning methods for Android malware identification", in 2019 7th Int. Symp. on Digital Forensics and Security (ISDFS), Varol, A., Karabatak, M., Varol, C. and Teke, S., Ed., Barcelos, IEEE, 2019

@inproceedings{lopes2019_1724520761022,
	author = "Lopes, J. P. and Serrão, C. and Nunes, L. and De Almeida,  A. and Oliveira, J.",
	title = "Overview of machine learning methods for Android malware identification",
	booktitle = "2019 7th International Symposium on Digital Forensics and Security (ISDFS)",
	year = "2019",
	editor = "Varol, A., Karabatak, M., Varol, C. and Teke, S.",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/ISDFS.2019.8757523",
	publisher = "IEEE",
	address = "Barcelos",
	organization = "IEEE",
	url = "https://ieeexplore.ieee.org/xpl/conhome/8750993/proceeding"
}

TY  - CPAPER
TI  - Overview of machine learning methods for Android malware identification
T2  - 2019 7th International Symposium on Digital Forensics and Security (ISDFS)
AU  - Lopes, J. P.
AU  - Serrão, C.
AU  - Nunes, L.
AU  - De Almeida,  A.
AU  - Oliveira, J.
PY  - 2019
DO  - 10.1109/ISDFS.2019.8757523
CY  - Barcelos
UR  - https://ieeexplore.ieee.org/xpl/conhome/8750993/proceeding
AB  - Mobile malware is growing and affecting more and more mobile users around the world. Malicious developers and organisations are disguising their malware payloads on apparently benign applications and pushing them to large app stores, such as Google Play Store, and from there to final users. App stores are currently losing the battle against malicious applications proliferation and existing malware. Detection methods based on signatures, such as those of an antivirus, are limited, new approaches based on machine learning start to be explored to surpass the limitations of traditional mobile malware detection methods, analysing not only static characteristics of the app but also its behaviour. This paper contains an overview of the existing machine learning mobile malware detection approaches based on static, dynamic and hybrid analysis, presenting the advantages and limitations of each, and a comparison between the reviewed methods.
ER  -