ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection
The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although applications are required to undergo malware screening before being published on official app stores, many malicious applications successfully evade detection by concealing sophisticated malware variants. These malicious behaviors are often activated only during runtime, making them difficult to identify through conventional static analysis. As a result, malware may remain undetected until after installation, potentially causing irreversible damage to users and their devices. This st...
| Authors | Md Faisal Ahmed, Zarin Tasnim Biash, Abu Raihan Shakil et al. |
|---|---|
| Primary category | cs.CR |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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