mlsanityData to 7 Oct 2026

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

arXiv 2109.03326 ↗First posted 5 Sep 2021cs.CR cs.LGcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 78 references that are in the map. First posted on arXiv 5 Sep 2021.

Builds on

15 of 78 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
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  7. 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
  8. 2017R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detectionscited by 15
  9. 2017MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)cited by 11
  10. 2019DL-Droid: Deep learning based android malware detection using real devicescited by 11
  11. 2017A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localizationcited by 8
  12. 2019Detecting and Classifying Android Malware using Static Analysis along with Creator Informationcited by 7
3 more
  1. 2013DroidAnalytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malwarecited by 3
  2. 2020Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learningcited by 2
  3. 2019Andro-Simnet: Android Malware Family Classification Using Social Network Analysiscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 4.

9 papers.

  1. 2022DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecodecited by 6
  2. 2022Self-Supervised Vision Transformers for Malware Detectioncited by 5
  3. 2024Unraveling the Key of Machine Learning-based Android Malware Detectioncited by 4
  4. 2022A two-steps approach to improve the performance of Android malware detectorscited by 2
  5. 2023Reassessing feature-based Android malware detection in a contemporary contextcited by 1
  6. 2023LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learningcited by 1
  7. 2024Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networkscited by 0
  8. 2024DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malwarecited by 0
  9. 2024Android Malware Detection Based on RGB Images and Multi-feature Fusioncited by 0