MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification

These are the two datasets -- EMBER Class and AZ Class to reproduce the results of the paper ``MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification", accepted to be published at the The 39th Annual AAAI Conference on Artificial Intelligence (AAAI) 2025.

• EMBER 2018 dataset
  We use the 2018 EMBER dataset, known for its challenging classification tasks, focusing on a subset of 337,035 malicious Windows PE files labeled by the top 100 malware families, each with over 400 samples. Features include file size, PE and COFF header details, DLL characteristics, imported and exported functions, and properties like size and entropy, all computed using the feature hashing trick.
  
  
• AZ-Class
  The AZ-Class dataset contains 285,582 samples from 100 Android malware families, each with at least 200 samples. We extracted Drebin features (Arp et al.2014) from the apps, covering eight categories like hardware access, permissions, API calls, and network addresses.