Ransomware is currently one of the most significant cyberthreats to both national infrastructure and the individual, often requiring severe treatment as an antidote. Triaging ran-somware based on its similarity with well-known ransomware samples is an imperative preliminary step in preventing a ransomware pandemic. Selecting the most appropriate triaging method can improve the precision of further static and dynamic analysis in addition to saving significant t ime a nd e ffort. Currently, the most popular and proven triaging methods are fuzzy hashing, import hashing and YARA rules, which can ascertain whether, or to what degree, two ransomware samples are similar to each other. However, the mechanisms of these three methods are quite different and their comparative assessment is difficult. Therefore, this paper presents an evaluation of these three methods for triaging the four most pertinent ransomware categories WannaCry, Locky, Cerber and CryptoWall. It evaluates their triaging performance and run-time system performance, highlighting the limitations of each method.
|Title of host publication||2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019|
|Publication status||Published - 10 Oct 2019|
|Event||2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019 - New Orleans, United States|
Duration: 23 Jun 2019 → 26 Jun 2019
|Name||IEEE International Conference on Fuzzy Systems|
|Conference||2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019|
|Period||23/06/19 → 26/06/19|
Bibliographical noteFunding Information:
The authors gratefully acknowledge the support of Hybrid-
© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- Context-Triggered Piecewise Hashing
- Fuzzy Hashing
- Import Hashing
- Similarity Preserving
- YARA Rules