首页|Identifying malicious accounts in blockchains using domain names and associated temporal properties

Identifying malicious accounts in blockchains using domain names and associated temporal properties

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The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals 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 the 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 tem-poral aspects attached to the DN.Our approach achieves 89.53％balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.

BlockchainMachine learningSuspect identificationDomain nameTemporal properties

Rohit Kumar Sachan、Rachit Agarwal、Sandeep Kumar Shukla

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C3i Hub,Indian Institute of Technology Kanpur,Kanpur 208016,India

Bennet University,Greater Noida 201310,India

CSE Department,Indian Institute of Technology Kanpur,Kanpur 208016,India

Merkle Science,Bangalore 560102,India

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National Blockchain Project at Indian Institute of Technology Kanpur,IndiaNational Cyber Security Coordinator's office of the Government of IndiaC3i Center funding from the Science and Engineering Research Board of the Government of India

NCSC/CS/2017518SERB/CS/2016466

2023

10.1016/j.bcra.2023.100136

区块链研究(英文)

区块链研究(英文)

EI
ISSN：
年,卷(期)：2023.4(3)
  • 58