Low-Resource Footprint, Data-Driven Malware Detection on Android-Reference-Cited by-同舟云学术

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Low-Resource Footprint, Data-Driven Malware Detection on Android

Published:2020-04-01 Issue:2 Volume:5 Page:213-222
ISSN:2377-3782
Container-title:IEEE Transactions on Sustainable Computing
language:
Short-container-title:IEEE Trans. Sustain. Comput.

Author:

Aonzo Simone,Merlo Alessio^ORCID,Migliardi Mauro^ORCID,Oneto Luca,Palmieri Francesco^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Control and Optimization,Hardware and Architecture,Renewable Energy, Sustainability and the Environment,Software

Link

http://xplorestaging.ieee.org/ielx7/7274860/9113128/08113505.pdf?arnumber=8113505

Cited by 18 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. A comprehensive review on permissions-based Android malware detection;International Journal of Information Security;2024-03-04

2. Dynamic data-driven resource allocation for NB-IoT performance in mobile devices;International Journal of Data Science and Analytics;2024-02-04

3. A review of detecting malware in android devices based on machine learning techniques;Expert Systems;2023-10-24

4. Success and failure rate prediction of Android Application using Machine Learning;2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON);2023-02-08

5. RealMalSol: real-time optimized model for Android malware detection using efficient neural networks and model quantization;Neural Computing and Applications;2023-02-04

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