# R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

arXiv: https://arxiv.org/abs/1705.04448 · HTML: https://mlsanity.com/paper/1705.04448/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2017.

## Built on by (most cited first, 15 of 15)

- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode](https://mlsanity.com/paper/2109.03326/) (2021)
- [DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode](https://mlsanity.com/paper/2212.05976/) (2022)
- [Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?](https://mlsanity.com/paper/2109.09654/) (2021)
- [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)
- [Towards interpreting ML-based automated malware detection models: a survey](https://mlsanity.com/paper/2101.06232/) (2021)
- [LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning](https://mlsanity.com/paper/2308.01413/) (2023)
- [Signal-Based Malware Classification Using 1D CNNs](https://mlsanity.com/paper/2509.06548/) (2025)
- [Comparison of Multiple Classifiers for Android Malware Detection with Emphasis on Feature Insights Using CICMalDroid 2020 Dataset](https://mlsanity.com/paper/2602.00058/) (2026)
- [Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection](https://mlsanity.com/paper/2204.05994/) (2022)
- [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)
- [A Visualized Malware Detection Framework with CNN and Conditional GAN](https://mlsanity.com/paper/2409.14439/) (2024)
- [LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors](https://mlsanity.com/paper/2512.21404/) (2025)

## Abstract (arXiv)

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.