Contributors

PE Parser was developed by Daniel Gibert during his PhD research to support the following research articles:

• Gibert, D., Mateu, C., Planes, J. et al. Using convolutional neural networks for classification of malware represented as images. J Comput Virol Hack Tech 15, 15–28 (2019). https://doi.org/10.1007/s11416-018-0323-0

• Gibert, D., Mateu, C., Planes, J. et al. HYDRA: A multimodal deep learning framework for malware classification. J. Computers & Security 95, 101873 (2020). https://doi.org/10.1016/j.cose.2020.101873

• Gibert, D., Mateu, C., Planes, J., & Vicens, R. (2018). Classification of Malware by Using Structural Entropy on Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11409

• Gibert, D., Mateu, C., Planes, J., Marques-Silva, J. et al. Auditing static machine learning anti-malware tools against metamorphic attacks. J. Computers & Security 102, 102159 (2021). https://doi.org/10.1016/j.cose.2020.102159

• Gibert, D., Planes, J., Mateu, C. Le, Q. Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classification. J. Expert Systems with Applications. 2022.