mlsanityData to 7 Oct 2026

Deep Multi-Task Learning for Malware Image Classification

arXiv 2405.05906 ↗First posted 9 May 2024cs.CR cs.CV cs.LGcited by 3

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Abstract (arXiv)

Malicious software is a pernicious global problem. A novel multi-task learning framework is proposed in this paper for malware image classification for accurate and fast malware detection. We generate bitmap (BMP) and (PNG) images from malware features, which we feed to a deep learning classifier. Our state-of-the-art multi-task learning approach has been tested on a new dataset, for which we have collected approximately 100,000 benign and malicious PE, APK, Mach-o, and ELF examples. Experiments with seven tasks tested with 4 activation functions, ReLU, LeakyReLU, PReLU, and ELU separately demonstrate that PReLU gives the highest accuracy of more than 99.87% on all tasks. Our model can effectively detect a variety of obfuscation methods like packing, encryption, and instruction overlapping, strengthing the beneficial claims of our model, in addition to achieving the state-of-art methods in terms of accuracy.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 65 references that are in the map. First posted on arXiv 9 May 2024.

Builds on

24 of 65 references.

References · 24

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12 more
  1. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  2. 2018Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Networkcited by 64
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  12. 2020Classifying Malware Images with Convolutional Neural Network Modelscited by 5

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024A Survey of Malware Detection Using Deep Learningcited by 8
  2. 2025Optimized detection of cyber-attacks on IoT networks via hybrid deep learning modelscited by 1
  3. 2025A Novel Active Learning Approach to Label One Million Unknown Malware Variantscited by 0