Visible to the public Cyberthreat Hunting - Part 1: Triaging Ransomware using Fuzzy Hashing, Import Hashing and YARA Rules

TitleCyberthreat Hunting - Part 1: Triaging Ransomware using Fuzzy Hashing, Import Hashing and YARA Rules
Publication TypeConference Paper
Year of Publication2019
AuthorsNaik, Nitin, Jenkins, Paul, Savage, Nick, Yang, Longzhi
Conference Name2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Date Publishedjun
KeywordsCer-ber, Cerber, composability, Context-Triggered Piecewise Hashing, cryptography, CryptoWall, cyberthreat hunting, data privacy, dynamic analysis, Engines, file organisation, Fuzzy Cryptography, Fuzzy Hashing, IM-PHASH, Import Hashing, invasive software, Locky, Metrics, program diagnostics, pubcrawl, ransomware, ransomware pandemic prevention, Resiliency, Scalability, SDHASH, Semantics, Similarity Preserving, SSDEEP, static analysis, Syntactics, System performance, Triaging, triaging performance, triaging ransomware, WannaCry, WannaCryptor, YARA rules
Abstract

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.

DOI10.1109/FUZZ-IEEE.2019.8858803
Citation Keynaik_cyberthreat_2019-1