Applying deep learning techniques for Android malware detection-Reference-Cited by-同舟云学术

Applying deep learning techniques for Android malware detection

Published:2018-09-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 11th International Conference on Security of Information and Networks
language:
Short-container-title:

Author:

Zegzhda Peter¹,Zegzhda Dmitry¹,Pavlenko Evgeny¹,Ignatev Gleb¹

Affiliation:

1. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3264437.3264476

Reference26 articles.

1. Mobile Operating System Market Share Worldwide http://gs.statcounter.com/os-market-share/mobile/worldwide. Mobile Operating System Market Share Worldwide http://gs.statcounter.com/os-market-share/mobile/worldwide.

2. Android vs iOS vs Windows: Which suffers most infections? Nokia reveals all https://www.zdnet.com/article/android-vs-ios-vs-windows-which-suffers-most-infections-nokia-reveals-all/. Android vs iOS vs Windows: Which suffers most infections? Nokia reveals all https://www.zdnet.com/article/android-vs-ios-vs-windows-which-suffers-most-infections-nokia-reveals-all/.

3. McAfee Mobile Threat Report https://www.mcafee.com/cn/resources/reports/rp-mobile-threat-report-2018.pdf. McAfee Mobile Threat Report https://www.mcafee.com/cn/resources/reports/rp-mobile-threat-report-2018.pdf.

4. Detecting Android application malicious behaviors based on the analysis of control flows and data flows

5. Application of clustering methods for analyzing the security of Android applications

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