  While machine learning is vulnerable to adversarial examples, it still lacks
systematic procedures and tools for evaluating its security in different
application contexts. In this article, we discuss how to develop automated and
scalable security evaluations of machine learning using practical attacks,
reporting a use case on Windows malware detection.
