Year: 2021
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Noever, David, Noever, Samantha Miller
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3203427025