Use of Multi-CNNs for Section Analysis in Static Malware Detection
Not on the map. Why?
Abstract (arXiv)
Existing research on malware detection focuses almost exclusively on the detection rate. However, in some cases, it is also important to understand the results of our algorithm, or to obtain more information, such as where to investigate in the file for an analyst. In this aim, we propose a new model to analyze Portable Executable files. Our method consists in splitting the files in different sections, then transform each section into an image, in order to train convolutional neural networks to treat specifically each identified section. Then we use all these scores returned by CNNs to compute a final detection score, using models that enable us to improve our analysis of the importance of each section in the final score.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 6 Feb 2024.
Builds on
4 of 11 references.
References · 4
- 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
- 2017Malware Detection by Eating a Whole EXEcited by 115
- 2020A Survey of Machine Learning Methods and Challenges for Windows Malware Classificationcited by 33
- 2022AI-based Malware and Ransomware Detection Modelscited by 7
Built on by
Citing papers per year: 2025: 1.
1 paper.
- 2025Semantic Preprocessing for LLM-based Malware Analysiscited by 3
- 2025Semantic Preprocessing for LLM-based Malware Analysis13 Jun 2025