# Deep Transfer Learning for Static Malware Classification

**Authors:** Li Chen  
**arXiv:** [1812.07606](https://arxiv.org/abs/1812.07606)  
**Date:** 2018-12-18  
**Summary page:** https://www.summarizepaper.com/en/paper/1812.07606v1/

## Key points

- Deep transfer learning is proposed for static malware classification
- Knowledge from natural images or objects is leveraged for malware detection
- Accelerated training time of deep neural networks while maintaining high classification performance
- Outperforms other classical machine learning methods in accuracy, false positive rate, true positive rate, and F1 score in binary classification
- Interpretation component incorporated to provide interpretable explanations for security practitioners
- Convex combination scheme of transfer learning and training from scratch for enhanced malware detection
- Algorithmic interpretation of vision-based malware classification techniques provided
- Promising method combining deep transfer learning with computer vision techniques
- Superiority over traditional machine learning approaches demonstrated
- Offers interpretability to enhance practical application in security settings

## Summary

In the paper "Deep Transfer Learning for Static Malware Classification" by Li Chen, the authors propose a novel approach to static malware classification using deep transfer learning from computer vision. The transfer learning scheme involves leveraging knowledge from natural images or objects and applying it to the domain of static malware detection. This allows for accelerated training time of deep neural networks while maintaining high classification performance. The effectiveness of the proposed approach is demonstrated through three experiments, where it outperforms other classical machine learning methods in terms of accuracy, false positive rate, true positive rate, and F1 score in binary classification. To enhance trust in the model, an interpretation component is incorporated into the algorithm providing interpretable explanations for security practitioners. Additionally, the paper discusses a convex combination scheme of transfer learning and training from scratch for enhanced malware detection. Insights into the algorithmic interpretation of vision-based malware classification techniques are also provided. Overall, this research presents a promising method for static malware classification that combines deep transfer learning with computer vision techniques. The results highlight its superiority over traditional machine learning approaches and offer interpretability to enhance its practical application in security settings.