Identifying malicious accounts in Blockchains using Domain Names and associated temporal properties (2106.13420v1)

Abstract: The rise in the adoption of blockchain technology has led to increased illegal activities by cyber-criminals costing billions of dollars. Many machine learning algorithms are applied to detect such illegal behavior. These algorithms are often trained on the transaction behavior and, in some cases, trained on the vulnerabilities that exist in the system. In our approach, we study the feasibility of using metadata such as Domain Name (DN) associated with the account in the blockchain and identify whether an account should be tagged malicious or not. Here, we leverage the temporal aspects attached to the DNs. Our results identify 144930 DNs that show malicious behavior, and out of these, 54114 DNs show persistent malicious behavior over time. Nonetheless, none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.