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

作　　者：Rohit Kumar Sachan Rachit Agarwal Sandeep Kumar Shukla 

机构地区：[1]C3i Hub,Indian Institute of Technology Kanpur,Kanpur 208016,India [2]CSE Department,Indian Institute of Technology Kanpur,Kanpur 208016,India [3]Bennet University,Greater Noida 201310,India [4]Merkle Science,Bangalore 560102,India

出　　处：《Blockchain(Research and Applications)》2023年第3期39-51,共13页区块链研究（英文）

基　　金：partially funded by the National Blockchain Project(grant number NCSC/CS/2017518)at Indian Institute of Technology Kanpur;India sponsored by the National Cyber Security Coordinator's office of the Government of India and partially by the C3i Center funding from the Science and Engineering Research Board of the Government of India(grant number SERB/CS/2016466).

摘　　要：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 temporal 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.

关 键 词：Blockchain Machine learning Suspect identification Domain name Temporal properties 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] TP181[自动化与计算机技术—计算机科学与技术]