.png){kind=icon}
.png){kind=icon}
Return
Feature-Driven Static Analysis for Learning-Based Android Malware Detection: A Review
.png){kind=icon}
OA
AI
DOI：10.1016/j.icte.2026.01.005.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The extensive embrace of Android has amplified malware risks, resulting in a need for better detection methods. This article investigates the area of static analysis, which analyses applications without execution by examining code and manifest files. We focus on studies from 2022–2025, regarding the feature extraction, datasets, feature selection, and approaches based on Machine Learning (ML) and Deep Learning (DL). We conclude by defining the major limitations and research gaps presented in studies regarding static analysis, and many insights for potential development of detection models that are efficient, accurate, and lightweight to improve detection patterns of Android malware.
Keywords:
Android
Static Analysis
Malware Detection
Machine Learning
Mobile Security
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
988
Citations:
2.5K