Machine-Burning

machine learning with a twist

Microsoft malware classification

This is a team project for Machine learning course on PMF, Zagreb. Team members are: Stanišić Matea, Škrabo Petra, Terzanović Mateja, Tolja Margarita.

Idea for project come from Kaggle. The main goal is to classify malware (using given .byte and .asm files) into one of the 9 families of malware:

• Ramnit
• Lollipop
• Kelihos_ver3
• Vundo
• Simba
• Tracur
• Kelihos_ver1
• Obfuscator.ACY
• Gatak

We helped ourselfs extracting the features from .byte and .asm files with this article, using their slightly modified code.

We are currently in developing phase - so, all our documentation in making you can track on overleaf.