  Several AutoML approaches have been proposed to automate the machine learning
(ML) process, such as searching for the ML model architectures and
hyper-parameters. However, these AutoML pipelines only focus on improving the
learning accuracy of benign samples while ignoring the ML model robustness
under adversarial attacks. As ML systems are increasingly being used in a
variety of mission-critical applications, improving the robustness of ML
systems has become of utmost importance. In this paper, we propose the first
robust AutoML framework, Robusta--based on reinforcement learning (RL)--to
perform feature selection, aiming to select features that lead to both accurate
and robust ML systems. We show that a variation of the 0-1 robust loss can be
directly optimized via an RL-based combinatorial search in the feature
selection scenario. In addition, we employ heuristics to accelerate the search
procedure based on feature scoring metrics, which are mutual information
scores, tree-based classifiers feature importance scores, F scores, and
Integrated Gradient (IG) scores, as well as their combinations. We conduct
extensive experiments and show that the proposed framework is able to improve
the model robustness by up to 22% while maintaining competitive accuracy on
benign samples compared with other feature selection methods.
