Synthetic Malware Generation

Overview

1. Embedding Generation – A vision transformer is used to create embeddings from binary images of malware samples.
2. Synthetic Data Generation – A Conditional WGAN-GP is trained to produce high-quality synthetic embeddings.
3. Classification Experiment – A classifier is trained on the synthetic embeddings and tested on real malware data to assess generalization.

Conclusion

1. The experiment aims to determine whether a classifier trained on synthetic malware embeddings can effectively classify real malware samples.
2. The classifier achieved a 94.9% classification accuracy, showing that synthetic data can be used to train classifiers.