Published in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2021
We present AIMED-RL, Automatic Intelligent Malware modifications to Evade Detection using Reinforcement Learning. Our approach is able to generate adversarial examples that lead machine learning models to misclassify malware files, without compromising their functionality. We implement our approach using a Distributional Double Deep Q-Network agent, adding a penalty to improve diversity of transformations. Thereby, we achieve competitive results compared to previous research based on reinforcement learning while minimizing the required sequence of transformations.