VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques-Reference-Cited by-同舟云学术

VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques

Published:2020-07-20 Issue:8 Volume:33 Page:3133-3153
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Bakour Khaled^ORCID,Ünver Halil Murat

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-020-05195-w.pdf

Reference43 articles.

1. Gartner (2018) Gartner says worldwide sales of smartphones recorded first ever decline during the fourth quarter of 2017 https://www.gartner.com/en/newsroom/press-releases/2018-02-22-gartner-says-worldwide-sales-of-smartphones-recorded-first-ever-decline-during-the-fourth-quarter-of-2017. Accessed 06 Apr 2019

2. McAfee (2018) AsiaHitGroup Gang Again sneaks billing-fraud apps onto google play. McAfee Labs threats report

3. G-Data (2018) The total count of mobile malware rises about 40 percent in 2018. https://www.gdatasoftware.com/blog/2018/11/31255-cyber-attacks-on-android-devices-on-the-rise. Accessed 01 Apr 2019

4. Micro T (2019) Adware disguised as game, TV, remote control apps infect 9 million Google play users https://blog.trendmicro.com/trendlabs-security-intelligence/adware-disguised-as-game-tv-remote-control-apps-infect-9-million-google-play-users/. Accessed 04 Apr 2019

5. Kirubavathi G, Anitha R (2017) Structural analysis and detection of android botnets using machine learning techniques. Int J Inf Secur 17(2):153–167. https://doi.org/10.1007/s10207-017-0363-3

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