  Ransomware uses encryption methods to make data inaccessible to legitimate
users. To date a wide range of ransomware families have been developed and
deployed, causing immense damage to governments, corporations, and private
users. As these cyberthreats multiply, researchers have proposed a range of
ransomware detection and classification schemes. Most of these methods use
advanced machine learning techniques to process and analyze real-world
ransomware binaries and action sequences. Hence this paper presents a survey of
this critical space and classifies existing solutions into several categories,
i.e., including network-based, host-based, forensic characterization, and
authorship attribution. Key facilities and tools for ransomware analysis are
also presented along with open challenges.
