mlsanityData to 7 Oct 2026

An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

arXiv 2103.13827 ↗First posted 24 Mar 2021cs.CR cs.LGcited by 5

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Abstract (arXiv)

In this paper, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Amongst our CNN experiments, transfer learning plays a prominent role specifically, we test the VGG-19 and ResNet152 models. As compared to previous work, the results presented in this paper are based on a larger and more diverse malware dataset, we consider a wider array of features, and we experiment with a much greater variety of learning techniques. Consequently, our results are the most comprehensive and complete that have yet been published.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 3 of its 41 references that are in the map. First posted on arXiv 24 Mar 2021.

Builds on

3 of 41 references.

References · 3

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  3. 2019Transfer Learning for Image-Based Malware Classificationcited by 12

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 1.

5 papers.

  1. 2022Generative Adversarial Networks and Image-Based Malware Classificationcited by 4
  2. 2023A Natural Language Processing Approach to Malware Classificationcited by 4
  3. 2021Auxiliary-Classifier GAN for Malware Analysiscited by 3
  4. 2025A Comparison of Selected Image Transformation Techniques for Malware Classificationcited by 1
  5. 2022BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Veccited by 0