# MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware

arXiv: https://arxiv.org/abs/2309.01350 · HTML: https://mlsanity.com/paper/2309.01350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [A Survey of Machine Learning Methods and Challenges for Windows Malware Classification](https://mlsanity.com/paper/2006.09271/) (2020)
- [Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints](https://mlsanity.com/paper/2108.04081/) (2021)
- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization](https://mlsanity.com/paper/2006.12402/) (2020)
- [Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning](https://mlsanity.com/paper/2106.05625/) (2021)

## Abstract (arXiv)

Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomings in the existing ML approaches are likely contributing to this problem. The majority of current ML approaches ignore real-world challenges such as the detection of novel malware. In addition, proposed ML approaches are often designed either for malware/benign-ware classification or malware family classification. Here we introduce and showcase preliminary capabilities of a new method that can perform precise identification of novel malware families, while also unifying the capability for malware/benign-ware classification and malware family classification into a single framework.