Background

In this project, we were tasked with developing machine learning models to classify obfuscated malware concealed within memory to evade any traditional detection methods. The dataset used for this project is called CIC_MalMem2022 dataset and contains memory-based malware samples that are curated to mimic real-world scenarios. The link to the dataset is here: https://www.unb.ca/cic/datasets/malmem-2022.html

Classification Tasks

The project has been divided into three parts to classify between the following criteria:

1. Classify Benign vs Malware
2. Classify Malware Category (Ransomware, spyware, Trojan, and Benign)
3. Classify Malware Variants