Malware Detection System (MDS) able to provide a significant contribution to defense against malicious attacks.
Table of Contents
According to NIST malware detection is a crucial factor in cybersecurity. Not only organizations but whoever uses a technological appliance should strive to detect and validate malware incidents rapidly to minimize the damage. Our goal is to develop a Malware Detection System (MDS) able to provide a significant contribution to defense against malicious attacks. Instead of traditional MDS that uses feature extractions ML algorithms, we thought that it could be interesting and challenging to develop a Convolutional Neural Network (CNN) trained to classify an executable file as malware or benign just analyzing the corresponding image. Traditionally, most MDS are based on feature vectors, which contain essential characteristics of malware. The two main categories of MDS are static and dynamic. In static analysis, malicious software is analyzed without executing it. The detection patterns used in static analysis include string signature, byte-sequence n-grams and syntactic library call. Instead, in dynamic analysis, malicious software is analyzed while being executed in a controlled environment (e.g., virtual machine, simulator, emulator, or sandbox). Before executing the malware samples, appropriate monitoring tools like Process Monitor or Capture BAT are installed and activated; thus to what have just been reported dynamic analysis is time consuming and computationally intensive.
- python3
- flet
- Clone the repo
git clone https://github.com/EdoardoAllegrini/Malware-Detection
- Install flet (https://flet.dev/docs/guides/python/getting-started/)
pip install flet
- If you want to get the model already trained and ready to use just contact me. Note: without the model you won't be able to properly use the tool.
- Run the GUI:
python3 src/gui.py
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Edoardo Allegrini - website
Samuele Bella - website
Project Link: https://github.com/EdoardoAllegrini/Malware-Detection