| Title | Ransomware Detection and Classification Strategies |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Vehabovic, Aldin, Ghani, Nasir, Bou-Harb, Elias, Crichigno, Jorge, Yayimli, Aysegül |
| Conference Name | 2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) |
| Date Published | jun |
| Keywords | composability, Computer crime, cybersecurity, Encryption, Forensics, Government, machine learning, Metrics, pubcrawl, ransomware, resilience, Resiliency |
| Abstract | 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 ransom ware 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. |
| DOI | 10.1109/BlackSeaCom54372.2022.9858296 |
| Citation Key | vehabovic_ransomware_2022 |
Ransomware Detection and Classification Strategies