Ransomware is on the rise fueled by high increase in ransomware payments. Ransomware are now delivered with new capabilities and evasion techniques to bypass many security controls causing threats to individuals and critical national infrastructures. For the sake of reducing time and effort creating new variant of Ransomware, mal ware authors tend to reuse codes from other malware or Ransomware. This can be an advantage for researchers to find similarities between two ransomware samples. Fuzzy hashing and import hashing are two methods that malware analysts use for malware identification and classification. YARA rules, on the other hand, becomes a pattern matching swiss knife that help malware researchers discover and clas sify malware. In this thesis, we aim to detect Ransomware executable PE files traversing over the network over non-secure protocols by ap plying MD5 hash, fuzzy hashing, and YARA rules detection methods while using import hash to detect good-ware files to reduce false pos itive detection. Since writing YARA rules is a time-consuming task that requires a high malware analysis knowledge, we generated rules manually and using auto YARA rule generator tool. The experiment results show that combining these methods can provide a promising detection result.