FABLDroid: Malware detection based on hybrid analysis with factor analysis and broad learning methods for android applications
The Android operating system, which is popular on mobile devices, creates concerns for users due to the malware it is exposed to. Android allows applications to be downloaded and installed outside the official application store. Applications installed from third-party environments threaten users’ pr...
Saved in:
Main Authors: | Kazım Kılıç, İsmail Atacak, İbrahim Alper Doğru |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-02-01
|
Series: | Engineering Science and Technology, an International Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098624003318 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers
by: Durmus Ozkan Sahin, et al.
Published: (2022-01-01) -
Android Malware Threats: A Strengthened Reverse Engineering Approach to Forensic Analysis
by: Ridho Surya Kusuma, et al.
Published: (2025-01-01) -
A novel feature selection technique: Detection and classification of Android malware
by: Sandeep Sharma, et al.
Published: (2025-03-01) -
DLCDroid an android apps analysis framework to analyse the dynamically loaded code
by: Rati Bhan, et al.
Published: (2025-01-01) -
Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
by: Yadigar Imamverdiyev, et al.
Published: (2025-01-01)