Use of Multi-CNNs for Section Analysis in Static Malware Detection
This work addresses the need for interpretable malware detection for security analysts, though it is incremental as it builds on existing CNN methods.
The paper tackles the problem of static malware detection by analyzing Portable Executable files through section-specific convolutional neural networks, achieving improved interpretability by identifying important sections for detection.
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.