Android malware detection gets smarter with machine learning techniques
Researchers have reviewed the latest methods for catching malicious Android apps before they run, combining code analysis with AI to spot threats faster and more accurately. As mobile threats surge, these lightweight detection tools could help companies and security firms protect millions of devices without slowing them down.
Originaltitel: Feature-Driven Static Analysis for Learning-Based Android Malware Detection: A Review
<p>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.</p>