A Context-Aware Android Malware Detection Approach Using Machine Learning
The Android platform has become the most popular smartphone operating system, which makes it a target for malicious mobile apps. This paper proposes a machine learning-based approach for Android malware detection based on application features. Unlike many prior research that focused exclusively on A...
Saved in:
| Main Authors: | Mohammed N. AlJarrah, Qussai M. Yaseen, Ahmad M. Mustafa |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/13/12/563 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Android Malware Category and Family Identification Using Parallel Machine Learning
by: Ahmed Hashem El Fiky, et al.
Published: (2022-07-01) -
Malware-SeqGuard: An Approach Utilizing LSTM and GRU for Effective Detection of Evolving Malware in Android Environments
by: Muhammad Usama Tanveer, et al.
Published: (2025-01-01) -
Multimodal Deep Learning for Android Malware Classification
by: James Arrowsmith, et al.
Published: (2025-02-01) -
A Review of Explainable AI for Android Malware Detection and Analysis
by: Maryam Tanha, et al.
Published: (2025-01-01) -
MALWARE DETECTION CAPABILITIES FOR MOBILE DEVICES RUNNING ANDROID OS BY MONITORING HARDWARE RESOURCE CONSUMPTION
by: Andrew M. Bonch-Bruevich, et al.
Published: (2025-07-01)