# Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid

arXiv: https://arxiv.org/abs/2405.03620 · HTML: https://mlsanity.com/paper/2405.03620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automated Synthesis of Semantic Malware Signatures using Maximum Satisfiability](https://mlsanity.com/paper/1608.06254/) (2016)
- [Aper: Evolution-Aware Runtime Permission Misuse Detection for Android Apps](https://mlsanity.com/paper/2201.12542/) (2022)
- [An investigation of the classifiers to detect android malicious apps](https://mlsanity.com/paper/1802.08611/) (2018)
- [Automated Inference of Software Library Usage Patterns](https://mlsanity.com/paper/1612.01626/) (2016)
- [Identifying Components from Object-Oriented APIs Based on Dynamic Analysis](https://mlsanity.com/paper/1803.06235/) (2018)
- [Kubernetes as an Availability Manager for Microservice Applications](https://mlsanity.com/paper/1901.04946/) (2019)
- [API Misuse Detection An Immune System inspired Approach](https://mlsanity.com/paper/2012.14078/) (2020)
- [A Kubernetes Controller for Managing the Availability of Elastic Microservice Based Stateful Applications](https://mlsanity.com/paper/2012.14086/) (2020)
- [Towards Using Package Centrality Trend to Identify Packages in Decline](https://mlsanity.com/paper/2107.10168/) (2021)
- [DPerm: Assisting the Migration of Android Apps to Runtime Permissions](https://mlsanity.com/paper/1706.05042/) (2017)
- [Runtime Permission Issues in Android Apps: Taxonomy, Practices, and Ways Forward](https://mlsanity.com/paper/2106.13012/) (2021)

## Built on by (most cited first, 1 of 1)

- [TIF: Learning Temporal Invariance in Android Malware Detectors](https://mlsanity.com/paper/2502.05098/) (2025)

## Abstract (arXiv)

As cyber threats and malware attacks increasingly alarm both individuals and businesses, the urgency for proactive malware countermeasures intensifies. This has driven a rising interest in automated machine learning solutions. Transformers, a cutting-edge category of attention-based deep learning methods, have demonstrated remarkable success. In this paper, we present BERTroid, an innovative malware detection model built on the BERT architecture. Overall, BERTroid emerged as a promising solution for combating Android malware. Its ability to outperform state-of-the-art solutions demonstrates its potential as a proactive defense mechanism against malicious software attacks. Additionally, we evaluate BERTroid on multiple datasets to assess its performance across diverse scenarios. In the dynamic landscape of cybersecurity, our approach has demonstrated promising resilience against the rapid evolution of malware on Android systems. While the machine learning model captures broad patterns, we emphasize the role of manual validation for deeper comprehension and insight into these behaviors. This human intervention is critical for discerning intricate and context-specific behaviors, thereby validating and reinforcing the model's findings.