Experimentat - Static Malware Analyzer in RUST

[rothoma2]

Background

This is our first Malware Static Analyzer written in RUST.
Static analysis is a well research topic, where recurrently Researches are able to achieve 95-97% detection using a series of features.

Requirements

• Write a Rust app, that uses certain library to parse and extract features from a Windows PE File Format. (exe, dll, etc)
• Use the following for references for the features that you want to extract.

1. https://github.com/HydraDragonAntivirus/HydraDragonAntivirus/blob/main/machinelearning/train.py
2. https://github.com/Anti-Malware-Alliance/research-papers/blob/main/Static%20Malware%20Detection%20and%20Analysis%20using%20Machine%20Learning%20Methods.pdf

• You can use the following datasets, or use daily malware o collect some malware samples for testing.

1. https://www.kaggle.com/datasets/albertozorzetto/cic-andmal-2020-dynamic-static-analysis
2. https://github.com/HydraDragonAntivirus/HydraDragonAntivirus?tab=readme-ov-file

You dont need to add all features, just some to demostrate the extraction and build of the train set.

The output should be in a pandas like format, row and columns, in a parquet file, to be using in a Python pipeline to train models.

[rothoma2]

You can also look into some of the Features from this model / project.

https://github.com/Kiinitix/Malware-Detection-using-Machine-learning