A dynamic DL-driven architecture to combat sophisticated Android malware

I Bibi, A Akhunzada, J Malik, J Iqbal, A Musaddiq… - IEEE …, 2020 - ieeexplore.ieee.org
The predominant Android operating system has captured enormous attention globally not
only in smart phone industry but also for varied smart devices. The open architecture and …

A dynamic robust DL-based model for android malware detection

IU Haq, TA Khan, A Akhunzada - IEEE Access, 2021 - ieeexplore.ieee.org
The dramatic increase in Android-based smart devices has brought technological revolution
to improve the overall quality of life and thus making it worth a billion-dollar market. Despite …

Android malware detection based on factorization machine

C Li, K Mills, D Niu, R Zhu, H Zhang, H Kinawi - IEEE Access, 2019 - ieeexplore.ieee.org
As the popularity of Android smart phones has increased in recent years, so too has the
number of malicious applications. Due to the potential for data theft that mobile phone users …

Mobidroid: A performance-sensitive malware detection system on mobile platform

R Feng, S Chen, X Xie, L Ma, G Meng… - … on Engineering of …, 2019 - ieeexplore.ieee.org
Currently, Android malware detection is mostly performed on the server side against the
increasing number of Android malware. Powerful computing resource gives more …

Famd: A fast multifeature android malware detection framework, design, and implementation

H Bai, N Xie, X Di, Q Ye - IEEE Access, 2020 - ieeexplore.ieee.org
With Android's dominant position within the current smartphone OS, increasing number of
malware applications pose a great threat to user privacy and security. Classification …

Hybrid-based malware analysis for effective and efficiency android malware detection

RB Hadiprakoso, H Kabetta… - … , Multimedia, Cyber and …, 2020 - ieeexplore.ieee.org
In the last decade, Android is the most widely used operating system. Despite this rapidly
increasing popularity, Android is also a target for the spread of malware. Android admits the …

A comprehensive survey on machine learning techniques for android malware detection

V Kouliaridis, G Kambourakis - Information, 2021 - mdpi.com
Year after year, mobile malware attacks grow in both sophistication and diffusion. As the
open source Android platform continues to dominate the market, malware writers consider it …

SAMADroid: a novel 3-level hybrid malware detection model for android operating system

S Arshad, MA Shah, A Wahid, A Mehmood… - IEEE …, 2018 - ieeexplore.ieee.org
For the last few years, Android is known to be the most widely used operating system and
this rapidly increasing popularity has attracted the malware developer's attention. Android …

Deep learning feature exploration for android malware detection

N Zhang, Y Tan, C Yang, Y Li - Applied Soft Computing, 2021 - Elsevier
Android mobile devices and applications are widely deployed and used in industry and
smart city. Malware detection is one of the most powerful and effective approaches to …

DroidMalwareDetector: A novel Android malware detection framework based on convolutional neural network

AT Kabakus - Expert Systems with Applications, 2022 - Elsevier
Smartphones have become an integral part of our daily lives thanks to numerous reasons.
While benefitting from what they offer, it is critical to be aware of the existence of malware in …