[HTML][HTML] Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey

F Nawshin, R Gad, D Unal, AK Al-Ali… - Computers and Electrical …, 2024 - Elsevier
Mobile devices have become an essential element in our day-to-day lives. The chances of
mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting …

Automated android malware detection using optimal ensemble learning approach for cybersecurity

H Alamro, W Mtouaa, S Aljameel, AS Salama… - IEEE …, 2023 - ieeexplore.ieee.org
Current technological advancement in computer systems has transformed the lives of
humans from real to virtual environments. Malware is unnecessary software that is often …

Early prediction of ransomware API calls behaviour based on GRU-TCN in healthcare IoT

J Jeon, S Baek, B Jeong, YS Jeong - Connection Science, 2023 - Taylor & Francis
The healthcare industry is collecting considerable patient and medical data by using Internet
of Things (IoT) devices. Consequently, ransomware attacks to encrypt healthcare systems or …

A comprehensive review on permissions-based Android malware detection

Y Sharma, A Arora - International Journal of Information Security, 2024 - Springer
The first Android-ready “G1” phone debuted in late October 2008. Since then, the growth of
Android malware has been explosive, analogous to the rise in the popularity of Android. The …

On deceiving malware classification with section injection

AA da Silva, M Pamplona Segundo - Machine Learning and Knowledge …, 2023 - mdpi.com
We investigate how to modify executable files to deceive malware classification systems.
This work's main contribution is a methodology to inject bytes across a malware file …

Intelligent Pattern Recognition using Equilibrium Optimizer with Deep Learning Model for Android Malware Detection

M Maray, M Maashi, HM Alshahrani, SS Aljameel… - IEEE …, 2024 - ieeexplore.ieee.org
Android malware recognition is the procedure of mitigating and identifying malicious
software (malware) planned to target Android operating systems (OS) that are extremely …

SNDGCN: Robust Android malware detection based on subgraph network and denoising GCN network

X Lu, J Zhao, S Zhu, P Lio - Expert Systems with Applications, 2024 - Elsevier
Android malware seriously affects the use of Android applications, and a growing number of
Android malware developers are using adversarial attacks to evade detection by deep …

An adaptive semi-supervised deep learning-based framework for the detection of Android malware.

A Wajahat, J He, N Zhu, T Mahmood… - J. Intell. Fuzzy …, 2023 - content.iospress.com
Positive developments in smartphone usage have led to an increase in malicious attacks,
particularly targeting Android mobile devices. Android has been a primary target for malware …

Ipanalyzer: A novel Android malware detection system using ranked intents and permissions

Y Sharma, A Arora - Multimedia Tools and Applications, 2024 - Springer
Android malware has been growing in scale and complexity, spurred by the unabated
uptake of smartphones worldwide. Millions of malicious Android applications have been …

[PDF][PDF] Covalent Bond Based Android Malware Detection Using Permission and System Call Pairs.

R Gupta, K Sharma, RK Garg - Computers, Materials & Continua, 2024 - cdn.techscience.cn
The prevalence of smartphones is deeply embedded in modern society, impacting various
aspects of our lives. Their versatility and functionalities have fundamentally changed how we …