Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks
With the widespread adoption of smartphones over the past decade, mobile applications have become a primary target for malicious attacks, usually in the form of malware. Recent studies have leveraged artificial intelligence (AI) techniques for malware detection and classification. However, applying...
Saved in:
| Main Authors: | Shamma Alshebli, Hyeran Mun, Deepak Puthal, Mohamed Jamal Zemerly, Luigi Martino, Ernesto Damiani, Chan Yeob Yeun |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11062843/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
GEAAD: generating evasive adversarial attacks against android malware defense
by: Naveed Ahmad, et al.
Published: (2025-04-01) -
A Survey on Adversarial Attacks for Malware Analysis
by: Kshitiz Aryal, et al.
Published: (2025-01-01) -
Coffee plant disease identification with an attentive multi-image segmentation framework (MISF) with CycleGAN
by: Savitri Kulkarni, et al.
Published: (2025-06-01) -
GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection
by: Yogesh Kumar Sharma, et al.
Published: (2025-01-01) -
A Novel Approach to Android Malware Intrusion Detection Using Zero-Shot Learning GANs
by: Syed Atir Raza Shirazi, et al.
Published: (2023-12-01)