DOAJ Open Access 2026

Feature-driven static analysis for learning-based android malware detection: A review

Sumesh Kharnotia Bhavna Arora Ravdeep Kour

Abstrak

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 to 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.

Topik & Kata Kunci

Information technology

Penulis (3)

Sumesh Kharnotia

Bhavna Arora

Ravdeep Kour

Format Sitasi

APA MLA BibTeX

Kharnotia, S., Arora, B., Kour, R. (2026). Feature-driven static analysis for learning-based android malware detection: A review. https://doi.org/10.1016/j.icte.2026.01.005

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Informasi Jurnal

Tahun Terbit: 2026
Sumber Database: DOAJ
DOI: 10.1016/j.icte.2026.01.005
Akses: Open Access ✓