mlsanityData to 14 Oct 2026

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

arXiv 1705.04448 ↗First posted 12 May 2017cs.CR cs.AIcited by 15

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 4; 2025: 2; 2026: 1.

15 papers.

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  1. 2021Deep Learning for Android Malware Defenses: a Systematic Literature Reviewcited by 18
  2. 2021DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecodecited by 9
  3. 2022DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecodecited by 6
  4. 2021Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?cited by 5
  5. 2024Unraveling the Key of Machine Learning-based Android Malware Detectioncited by 4
  6. 2022A two-steps approach to improve the performance of Android malware detectorscited by 2
  7. 2021Towards interpreting ML-based automated malware detection models: a surveycited by 1
  8. 2023LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learningcited by 1
  9. 2025Signal-Based Malware Classification Using 1D CNNscited by 1
  10. 2026Comparison of Multiple Classifiers for Android Malware Detection with Emphasis on Feature Insights Using CICMalDroid 2020 Datasetcited by 1
  11. 2022Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detectioncited by 0
  12. 2024DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malwarecited by 0
3 more
  1. 2024Android Malware Detection Based on RGB Images and Multi-feature Fusioncited by 0
  2. 2024A Visualized Malware Detection Framework with CNN and Conditional GANcited by 0
  3. 2025LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectorscited by 0