# Android Malware Detection: A Machine Leaning Approach

arXiv: https://arxiv.org/abs/2511.00894 · HTML: https://mlsanity.com/paper/2511.00894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 2 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [A Preliminary Study On the Sustainability of Android Malware Detection](https://mlsanity.com/paper/1807.08221/) (2018)
- [Detecting malicious logins as graph anomalies](https://mlsanity.com/paper/1909.09047/) (2019)
- [Dataset Bias in Android Malware Detection](https://mlsanity.com/paper/2205.15532/) (2022)
- [Subspace Determination through Local Intrinsic Dimensional Decomposition: Theory and Experimentation](https://mlsanity.com/paper/1907.06771/) (2019)
- [Deep Neural Mobile Networking](https://mlsanity.com/paper/2011.05267/) (2020)

## Abstract (arXiv)

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and real-world applicability. Key findings show that ensemble methods demonstrate superior performance, but there are trade-offs between model interpretability, efficiency, and accuracy. Given its increasing threat, the insights guide future research and practical use of ML to combat Android malware.