# Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability

arXiv: https://arxiv.org/abs/2107.11100 · HTML: https://mlsanity.com/paper/2107.11100/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [Non-Negative Networks Against Adversarial Attacks](https://mlsanity.com/paper/1806.06108/) (2018)
- [Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey](https://mlsanity.com/paper/1406.7061/) (2014)
- [A Convolutional Transformation Network for Malware Classification](https://mlsanity.com/paper/1909.07227/) (2019)

## Built on by (most cited first, 9 of 9)

- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [AI-based Malware and Ransomware Detection Models](https://mlsanity.com/paper/2207.02108/) (2022)
- [Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2409.13723/) (2024)
- [Quantum Machine Learning for Malware Classification](https://mlsanity.com/paper/2305.09674/) (2023)
- [A Survey of Machine Learning Algorithms for Detecting Ransomware Encryption Activity](https://mlsanity.com/paper/2110.07636/) (2021)
- [Towards an in-depth detection of malware using distributed QCNN](https://mlsanity.com/paper/2312.12161/) (2023)
- [Explainable Malware Detection with Tailored Logic Explained Networks](https://mlsanity.com/paper/2405.03009/) (2024)
- [A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware](https://mlsanity.com/paper/2111.02388/) (2021)
- [MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware](https://mlsanity.com/paper/2309.01350/) (2023)

## Abstract (arXiv)

Malware detection and analysis are active research subjects in cybersecurity over the last years. Indeed, the development of obfuscation techniques, as packing, for example, requires special attention to detect recent variants of malware. The usual detection methods do not necessarily provide tools to interpret the results. Therefore, we propose a model based on the transformation of binary files into grayscale image, which achieves an accuracy rate of 88%. Furthermore, the proposed model can determine if a sample is packed or encrypted with a precision of 85%. It allows us to analyze results and act appropriately. Also, by applying attention mechanisms on detection models, we have the possibility to identify which part of the files looks suspicious. This kind of tool should be very useful for data analysts, it compensates for the lack of interpretability of the common detection models, and it can help to understand why some malicious files are undetected.