# The Curious Case of Machine Learning In Malware Detection

arXiv: https://arxiv.org/abs/1905.07573 · HTML: https://mlsanity.com/paper/1905.07573/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2019.

## Built on by (most cited first, 4 of 4)

- [Malware Detection and Prevention using Artificial Intelligence Techniques](https://mlsanity.com/paper/2206.12770/) (2022)
- [Towards Interpretable Ensemble Learning for Image-based Malware Detection](https://mlsanity.com/paper/2101.04889/) (2021)
- [Towards interpreting ML-based automated malware detection models: a survey](https://mlsanity.com/paper/2101.06232/) (2021)
- [Recent Advancements in Machine Learning For Cybercrime Prediction](https://mlsanity.com/paper/2304.04819/) (2023)

## Abstract (arXiv)

In this paper, we argue that machine learning techniques are not ready for malware detection in the wild. Given the current trend in malware development and the increase of unconventional malware attacks, we expect that dynamic malware analysis is the future for antimalware detection and prevention systems. A comprehensive review of machine learning for malware detection is presented. Then, we discuss how malware detection in the wild present unique challenges for the current state-of-the-art machine learning techniques. We defined three critical problems that limit the success of malware detectors powered by machine learning in the wild. Next, we discuss possible solutions to these challenges and present the requirements of next-generation malware detection. Finally, we outline potential research directions in machine learning for malware detection.