Identifying malicious accounts in blockchains using domain names and associated temporal properties
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, tr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
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| Series: | Blockchain: Research and Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096720923000118 |
| _version_ | 1797660145815126016 |
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| author | Rohit Kumar Sachan Rachit Agarwal Sandeep Kumar Shukla |
| author_facet | Rohit Kumar Sachan Rachit Agarwal Sandeep Kumar Shukla |
| author_sort | Rohit Kumar Sachan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:25:33Z |
| format | Article |
| id | doaj.art-e61a94dc88ce422180d32e2db925377e |
| institution | Directory Open Access Journal |
| issn | 2666-9536 |
| language | English |
| last_indexed | 2024-03-11T18:25:33Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Blockchain: Research and Applications |
| spelling | doaj.art-e61a94dc88ce422180d32e2db925377e2023-10-14T04:44:30ZengElsevierBlockchain: Research and Applications2666-95362023-09-0143100136Identifying malicious accounts in blockchains using domain names and associated temporal propertiesRohit Kumar Sachan0Rachit Agarwal1Sandeep Kumar Shukla2C3i Hub, Indian Institute of Technology Kanpur, Kanpur 208016, India; Bennet University, Greater Noida 201310, India; Corresponding author. C3i Hub, Indian Institute of Technology Kanpur, Kanpur 208016, India.CSE Department, Indian Institute of Technology Kanpur, Kanpur 208016, India; Merkle Science, Bangalore 560102, IndiaCSE Department, Indian Institute of Technology Kanpur, Kanpur 208016, IndiaThe 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.http://www.sciencedirect.com/science/article/pii/S2096720923000118BlockchainMachine learningSuspect identificationDomain nameTemporal properties |
| spellingShingle | Rohit Kumar Sachan Rachit Agarwal Sandeep Kumar Shukla Identifying malicious accounts in blockchains using domain names and associated temporal properties Blockchain: Research and Applications Blockchain Machine learning Suspect identification Domain name Temporal properties |
| title | Identifying malicious accounts in blockchains using domain names and associated temporal properties |
| title_full | Identifying malicious accounts in blockchains using domain names and associated temporal properties |
| title_fullStr | Identifying malicious accounts in blockchains using domain names and associated temporal properties |
| title_full_unstemmed | Identifying malicious accounts in blockchains using domain names and associated temporal properties |
| title_short | Identifying malicious accounts in blockchains using domain names and associated temporal properties |
| title_sort | identifying malicious accounts in blockchains using domain names and associated temporal properties |
| topic | Blockchain Machine learning Suspect identification Domain name Temporal properties |
| url | http://www.sciencedirect.com/science/article/pii/S2096720923000118 |
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