A Survey on Detection and Classification of Ransomware Bitcoin Transactions

Computer Science Engineering

Ransomware transactions are widely used in crypto-currencies. Most modern ransomware use Bitcoin for payments. The Bitcoin transactions are permanently recorded and publicly available. Based on the collected ransomware related Bitcoin addresses, the researchers have employed advanced data analytics techniques to detect ransomware related transactions and malicious Bitcoin addresses automatically. The machine learning approaches are evaluated based on Bit-Heist Ransomware dataset which is publicly available. Various approaches are used for it. The machine learning approaches are evaluated based on the patterns differentiating such cybercrime operations from normal bit-coin transactions in order to identify and report attacks. Different experimental results show that this model achieved improved detection rate skill of the model in classification. A survey for all these techniques is in this paper for analyzing various algorithms and methods. This work would be the ultimate opportunity to expand and organize research efforts in future wok by applying some early preventions and strategies to defeat these kinds of malicious transactions.

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