# DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode

arXiv: https://arxiv.org/abs/2109.03326 · HTML: https://mlsanity.com/paper/2109.03326/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 78 references that are in the map.
- First posted on arXiv 5 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections](https://mlsanity.com/paper/1705.04448/) (2017)
- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)](https://mlsanity.com/paper/1711.07477/) (2017)
- [DL-Droid: Deep learning based android malware detection using real devices](https://mlsanity.com/paper/1911.10113/) (2019)
- [A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization](https://mlsanity.com/paper/1704.01759/) (2017)
- [Detecting and Classifying Android Malware using Static Analysis along with Creator Information](https://mlsanity.com/paper/1903.01618/) (2019)
- [DroidAnalytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware](https://mlsanity.com/paper/1302.7212/) (2013)
- [Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning](https://mlsanity.com/paper/2002.03594/) (2020)
- [Andro-Simnet: Android Malware Family Classification Using Social Network Analysis](https://mlsanity.com/paper/1906.09456/) (2019)

## Built on by (most cited first, 9 of 9)

- [Self-Supervised Vision Transformers for Malware Detection](https://mlsanity.com/paper/2208.07049/) (2022)
- [DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode](https://mlsanity.com/paper/2212.05976/) (2022)
- [Unraveling the Key of Machine Learning-based Android Malware Detection](https://mlsanity.com/paper/2402.02953/) (2024)
- [A two-steps approach to improve the performance of Android malware detectors](https://mlsanity.com/paper/2205.08265/) (2022)
- [Reassessing feature-based Android malware detection in a contemporary context](https://mlsanity.com/paper/2301.12778/) (2023)
- [LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning](https://mlsanity.com/paper/2308.01413/) (2023)
- [Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/2403.00890/) (2024)
- [DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware](https://mlsanity.com/paper/2408.16353/) (2024)
- [Android Malware Detection Based on RGB Images and Multi-feature Fusion](https://mlsanity.com/paper/2408.16555/) (2024)

## Abstract (arXiv)

Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such as malware detection, where deep learning on images alleviates the need for comprehensively hand-crafted features generalising to different malware variants. We postulate that this research direction could become the next frontier in Android malware detection, and therefore requires a clear roadmap to ensure that new approaches indeed bring novel contributions. We contribute with a first building block by developing and assessing a baseline pipeline for image-based malware detection with straightforward steps. We propose DexRay, which converts the bytecode of the app DEX files into grey-scale "vector" images and feeds them to a 1-dimensional Convolutional Neural Network model. We view DexRay as foundational due to the exceedingly basic nature of the design choices, allowing to infer what could be a minimal performance that can be obtained with image-based learning in malware detection. The performance of DexRay evaluated on over 158k apps demonstrates that, while simple, our approach is effective with a high detection rate (F1-score= 0.96). Finally, we investigate the impact of time decay and image-resizing on the performance of DexRay and assess its resilience to obfuscation. This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain.