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 …

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 …

MAPAS: a practical deep learning-based android malware detection system

J Kim, Y Ban, E Ko, H Cho, JH Yi - International Journal of Information …, 2022 - Springer
A lot of malicious applications appears every day, threatening numerous users. Therefore, a
surge of studies have been conducted to protect users from newly emerging malware by …

A hybrid feature selection approach-based Android malware detection framework using machine learning techniques

SK Smmarwar, GP Gupta, S Kumar - Cyber Security, Privacy and …, 2022 - Springer
With more popularity and advancement in Internet-based services, the use of the Android
smartphone has been increasing very rapidly. The tremendous popularity of using the …

FCSCNN: Feature centralized Siamese CNN-based android malware identification

K Kong, Z Zhang, ZY Yang, Z Zhang - Computers & Security, 2022 - Elsevier
Malware has been a serious threat to Android security since its inception. The key to
defending against malware threats is how to extract the discriminative features of the apps …

Mlifdect: android malware detection based on parallel machine learning and information fusion

X Wang, D Zhang, X Su, W Li - Security and Communication …, 2017 - Wiley Online Library
In recent years, Android malware has continued to grow at an alarming rate. More recent
malicious apps' employing highly sophisticated detection avoidance techniques makes the …

GA-StackingMD: Android malware detection method based on genetic algorithm optimized stacking

N Xie, Z Qin, X Di - Applied Sciences, 2023 - mdpi.com
With the rapid development of network and mobile communication, intelligent terminals such
as smartphones and tablet computers have changed people's daily life and work. However …

Identification of significant permissions for efficient android malware detection

H Rathore, SK Sahay, R Rajvanshi… - International conference on …, 2020 - Springer
Abstract Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs,
ie volume, velocity and variety. A recent report indicates that one out of every five …

A novel android malware detection approach based on convolutional neural network

Y Zhang, Y Yang, X Wang - … of the 2nd international conference on …, 2018 - dl.acm.org
With the explosive growth of Android malware, there is a pressure for us to improve the
performance of existing malware detection approaches. In this paper, we proposed …

[HTML][HTML] DL-AMDet: Deep learning-based malware detector for android

AR Nasser, AM Hasan, AJ Humaidi - Intelligent Systems with Applications, 2024 - Elsevier
The Android operating system, with its market share leadership and open-source nature in
smartphones, has become the primary target of malware. However, detecting malicious …