A Robust Malware Detection Approach for Android System Based on Ensemble Learning-Reference-Cited by-同舟云学术

A Robust Malware Detection Approach for Android System Based on Ensemble Learning

Published:2022 Issue: Volume: Page:309-321
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Li Wenjia,Cai Juecong,Wang Zi,Cheng Sihua

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-0468-4_23

Reference37 articles.

1. O'Dea, S.: Market share of mobile operating systems worldwide 2012–2021. https://www.statista.com/statistics/272698/global-market-share-held-by-mobile-operating-systems-since-2009/. Accessed 29 June 2021

2. Cisco. Midyear Security Report (2015). http://www.cisco.com/web/offers/pdfs/cisco-msr-2015.pdf

3. Trend Micro. A Look at Google Bouncer (2012). http://blog.trendmicro.com/trendlabs-security-intelligence/a-look-at-google-bouncer/

4. QEMU. 2016. http://wiki.qemu.org/Main_Page

5. Stefanko, L.: Android Trojan drops in, despite Google’s Bouncer. ESET, 22 September 2015–12:48 pm (2015). http://www.welivesecurity.com/2015/09/22/android-trojan-drops-in-despite-googles-bouncer/

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Android Malware Detection: A Literature Review;Communications in Computer and Information Science;2023

2. Source Code Vulnerability Detection Using Deep Learning Algorithms for Industrial Applications;Communications in Computer and Information Science;2023

3. Malware Traffic Classification Based on GAN and BP Neural Networks;Communications in Computer and Information Science;2023