  This research recasts ransomware detection using performance monitoring and
statistical machine learning. The work builds a test environment with 41 input
variables to label and compares three computing states: idle, encryption and
compression. A common goal of this behavioral detector seeks to anticipate and
short-circuit the final step of hard-drive locking with encryption and the
demand for payment to return the file system to its baseline. Comparing machine
learning techniques, linear regression outperforms random forest, decision
trees, and support vector machines (SVM). All algorithms classified the 3
possible classes (idle, encryption, and compression) with greater than 91%
accuracy.
