The BitcoinHeist: Classifications of Ransomware Crime Families

Ransomware attacks are on the rise and attackers are hijacking valuable information from different critical infrastructures and businesses requiring ransom payments to release the encrypted files. Payments in cryptocurrencies are designed to evade tracing the transactions and the recipients. With anonymity being paramount, tracing cryptocurrencies payments due to malicious activity and criminal transactions is a complicated process. Therefore, the need to identify these transactions and label them is crucial to categorize them as legitimate digital currency trade and exchange or malicious activity operations. Machine learning techniques are utilized to train the machine to recognize specific transactions and trace them back to malicious transactions or benign ones. I propose to work on the Bitcoin Heist data set to classify the different malicious transactions. The different transactions features are analyzed to predict a classifier label among the classifiers that have been identified as ransomware or associated with malicious activity. I use decision tree classifiers and ensemble learning to implement a random forest classifier. Results are assessed to evaluate accuracy, precision, and recall. I limit the study design to known ransomware identified previously and made available under the Bitcoin transaction graph from January 2009 to December 2018.