# Deep Multi-Task Learning for Malware Image Classification

arXiv: https://arxiv.org/abs/2405.05906 · HTML: https://mlsanity.com/paper/2405.05906/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network](https://mlsanity.com/paper/1802.09178/) (2018)
- [Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods](https://mlsanity.com/paper/1712.09677/) (2017)
- [Survey of Machine Learning Techniques for Malware Analysis](https://mlsanity.com/paper/1710.08189/) (2017)
- [Imbalanced Malware Images Classification: a CNN based Approach](https://mlsanity.com/paper/1708.08042/) (2017)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [R-CNNs for Pose Estimation and Action Detection](https://mlsanity.com/paper/1406.5212/) (2014)
- [DL-Droid: Deep learning based android malware detection using real devices](https://mlsanity.com/paper/1911.10113/) (2019)
- [Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back](https://mlsanity.com/paper/1803.04062/) (2018)
- [ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performance](https://mlsanity.com/paper/2012.07564/) (2020)
- [Improved Inception-Residual Convolutional Neural Network for Object Recognition](https://mlsanity.com/paper/1712.09888/) (2017)
- [Classifying Malware Images with Convolutional Neural Network Models](https://mlsanity.com/paper/2010.16108/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Survey of Malware Detection Using Deep Learning](https://mlsanity.com/paper/2407.19153/) (2024)
- [Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models](https://mlsanity.com/paper/2502.11470/) (2025)
- [A Novel Active Learning Approach to Label One Million Unknown Malware Variants](https://mlsanity.com/paper/2507.02959/) (2025)

## 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.