  The damage caused by crypto-ransomware, due to encryption, is difficult to
revert and cause data losses. In this paper, a machine learning (ML) classifier
was built to early detect ransomware (called crypto-ransomware) that uses
cryptography by program behavior. If a signature-based detection was missed, a
behavior-based detector can be the last line of defense to detect and contain
the damages. We find that input/output activities of ransomware and the
file-content entropy are unique traits to detect crypto-ransomware. A
deep-learning (DL) classifier can detect ransomware with a high accuracy and a
low false positive rate. We conduct an adversarial research against the models
generated. We use simulated ransomware programs to launch a gray-box analysis
to probe the weakness of ML classifiers and to improve model robustness. In
addition to accuracy and resiliency, trustworthiness is the other key criteria
for a quality detector. Making sure that the correct information was used for
inference is important for a security application. The Integrated Gradient
method was used to explain the deep learning model and also to reveal why false
negatives evade the detection. The approaches to build and to evaluate a
real-world detector were demonstrated and discussed.
