Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk
Abstract Ethereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the sc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-10-01
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| Series: | Cybersecurity |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42400-023-00180-x |
| _version_ | 1827797060325212160 |
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| author | Chuyi Yan Chen Zhang Meng Shen Ning Li Jinhao Liu Yinhao Qi Zhigang Lu Yuling Liu |
| author_facet | Chuyi Yan Chen Zhang Meng Shen Ning Li Jinhao Liu Yinhao Qi Zhigang Lu Yuling Liu |
| author_sort | Chuyi Yan |
| collection | DOAJ |
| description | Abstract Ethereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the scam behavior on Ethereum and investigate it at the node interaction level. Based on the life cycle and risk identification points we found, we propose an automatic detection model named Aparecium. First, a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors. Second, the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors. Conducting experiments in the wild Ethereum datasets, we prove Aparecium is effective which the precision, recall and F1-score achieve at 0.977, 0.957 and 0.967 respectively. |
| first_indexed | 2024-03-11T19:16:54Z |
| format | Article |
| id | doaj.art-3486d1198171405fb8c0256ebeae28eb |
| institution | Directory Open Access Journal |
| issn | 2523-3246 |
| language | English |
| last_indexed | 2024-03-11T19:16:54Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj.art-3486d1198171405fb8c0256ebeae28eb2023-10-08T11:19:52ZengSpringerOpenCybersecurity2523-32462023-10-016111610.1186/s42400-023-00180-xAparecium: understanding and detecting scam behaviors on Ethereum via biased random walkChuyi Yan0Chen Zhang1Meng Shen2Ning Li3Jinhao Liu4Yinhao Qi5Zhigang Lu6Yuling Liu7Institute of Information Engineering, Chinese Academy of SciencesInstitute of Information Engineering, Chinese Academy of SciencesSchool of Cyberspace Science and Technology, Beijing Institute of TechnologyInstitute of Information Engineering, Chinese Academy of SciencesInstitute of Information Engineering, Chinese Academy of SciencesInstitute of Information Engineering, Chinese Academy of SciencesInstitute of Information Engineering, Chinese Academy of SciencesInstitute of Information Engineering, Chinese Academy of SciencesAbstract Ethereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the scam behavior on Ethereum and investigate it at the node interaction level. Based on the life cycle and risk identification points we found, we propose an automatic detection model named Aparecium. First, a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors. Second, the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors. Conducting experiments in the wild Ethereum datasets, we prove Aparecium is effective which the precision, recall and F1-score achieve at 0.977, 0.957 and 0.967 respectively.https://doi.org/10.1186/s42400-023-00180-xBlockchainNetwork securityEthereumScam detectionBehavior understanding |
| spellingShingle | Chuyi Yan Chen Zhang Meng Shen Ning Li Jinhao Liu Yinhao Qi Zhigang Lu Yuling Liu Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk Cybersecurity Blockchain Network security Ethereum Scam detection Behavior understanding |
| title | Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk |
| title_full | Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk |
| title_fullStr | Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk |
| title_full_unstemmed | Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk |
| title_short | Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk |
| title_sort | aparecium understanding and detecting scam behaviors on ethereum via biased random walk |
| topic | Blockchain Network security Ethereum Scam detection Behavior understanding |
| url | https://doi.org/10.1186/s42400-023-00180-x |
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