ml-lab / malware-detection-using-machine-learning

Multi-class malware classification using Deep Learning

Malware Detection Using Machine Learning

This repository contains the source code for detecting different type of malwares using Deep learning based Feature Extraction and Wraper based Feature Selection Technique. A research paper describing how it works is availible at "to be updated"

Two major approaches we used for malware classification: 1- Image representation of byte file Independent of the platform It requires No knowledge of domain like assembly instructions 2- Hybrid feature space using both ASM and byte file This approach is platform dependent but gives a better performance that using byte file. Requires huge resources and processing time.

The data used in these tutorial can be found on the Hybrid(Final) folder of following drive link:

https://drive.google.com/drive/folders/1s7EC4s_-hP9q5vEhs-3vAubspcZbBADK?usp=sharing

After downloading the required dataset, following is the sequence of files in the hybrid folder whose execution will lead to results.

1. "Creating hybrid dataset"

2. "Min-max normalization(hybrid dataset)"

3. "ANN-Results"

The project was done under the guidance of Dr. Asifullah Khan, DCIS, PIEAS.