1. Al-Dujaili, A., Huang, A., Hemberg, E., O’Reilly, U.M.: Adversarial deep learning for robust detection of binary encoded malware. In: IEEE Security and Privacy Workshops (S &PW) (2018)
2. Anderson, H.S., Roth, P.: Ember: an open dataset for training static PE malware machine learning models. arXiv preprint arXiv:1804.04637 (2018)
3. Anderson, R., et al.: Measuring the changing cost of cybercrime. In: Workshop on the Economics of Information Security (WEIS) (2019)
4. Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., Siemens, C.: Drebin: effective and explainable detection of android malware in your pocket. In: Network and Distributed System Security Symposium (NDSS) (2014)
5. Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In: International Conference on Machine Learning (ICML) (2018)