# Deep Learning for Android Malware Defenses: a Systematic Literature Review

arXiv: https://arxiv.org/abs/2103.05292 · HTML: https://mlsanity.com/paper/2103.05292/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 256 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 30 of 61)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)

## Built on by (most cited first, 18 of 18)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting](https://mlsanity.com/paper/2309.01866/) (2023)
- [It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness](https://mlsanity.com/paper/2303.09767/) (2023)
- [Investigating White-Box Attacks for On-Device Models](https://mlsanity.com/paper/2402.05493/) (2024)
- [PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks](https://mlsanity.com/paper/2302.11328/) (2023)
- [Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora](https://mlsanity.com/paper/2205.13022/) (2022)
- [A two-steps approach to improve the performance of Android malware detectors](https://mlsanity.com/paper/2205.08265/) (2022)
- [Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response](https://mlsanity.com/paper/2408.01999/) (2024)
- [Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems](https://mlsanity.com/paper/2501.13782/) (2025)
- [Energy-bounded Learning for Robust Models of Code](https://mlsanity.com/paper/2112.11226/) (2021)
- [MASKDROID: Robust Android Malware Detection with Masked Graph Representations](https://mlsanity.com/paper/2409.19594/) (2024)
- [Protect Your Secrets: Understanding and Measuring Data Exposure in VSCode Extensions](https://mlsanity.com/paper/2412.00707/) (2024)
- [VOLTRON: Detecting Unknown Malware Using Graph-Based Zero-Shot Learning](https://mlsanity.com/paper/2507.04275/) (2025)
- [Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection](https://mlsanity.com/paper/2204.05994/) (2022)
- [Evaluating the robustness of adversarial defenses in malware detection systems](https://mlsanity.com/paper/2505.09342/) (2025)
- [MeLeMaD: Adaptive Malware Detection via Chunk-wise Feature Selection and Meta-Learning](https://mlsanity.com/paper/2512.23987/) (2025)
- [HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning](https://mlsanity.com/paper/2609.26352/) (2026)

## Abstract (arXiv)

Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing deep learning techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on deep learning approaches for Android Malware defenses exists. In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014-2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based on Android malware detection, 53 primary studies (40.1 percent) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.