A Method of Mining Key Accounts from Internet Pyramid Selling Data
Internet pyramid selling causes great harm and has difficulty in obtaining evidence. As most of internet pyramid selling often uses virtual coins to complete cash settlement, criminals can exchange virtual coins by controlling visual accounts to transfer funds illegally. The purpose of the work is t...
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2019-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/322637 |
| _version_ | 1797207439361179648 |
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| author | Jianying Xiong |
| author_facet | Jianying Xiong |
| author_sort | Jianying Xiong |
| collection | DOAJ |
| description | Internet pyramid selling causes great harm and has difficulty in obtaining evidence. As most of internet pyramid selling often uses virtual coins to complete cash settlement, criminals can exchange virtual coins by controlling visual accounts to transfer funds illegally. The purpose of the work is to mine the key accounts and build evidence chain of personnel and funds. In order to deal with a large number of transaction data, we characterize the virtual currency trading data, which is divided into seven transaction characteristics. The key accounts are these outliers which have too prominent trading behaviour. So we use positive samples to train One Class Support Vector Machine (OCSVM) classification to find trading behaviours’ boundary and detect outliers in classification. Then, the correlation between member’s information and pyramid selling hierarchical relationship can also be obtained by further linking the trading behaviour of key accounts. Finally, we can screen out the virtual accounts directly controlled by the pyramid selling organization, analyse the flow of funds, investigate and obtain evidence on amount of money involved in pyramid selling. The experimental results show that the classification model cannot only establish the normal model for account transfer behaviour, but also effectively identify abnormal transfer behaviour, thus improving efficiency of investigation and evidence collection for economic investigation departments. |
| first_indexed | 2024-04-24T09:22:56Z |
| format | Article |
| id | doaj.art-ceca6b68b2044678856de478126bd670 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:22:56Z |
| publishDate | 2019-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-ceca6b68b2044678856de478126bd6702024-04-15T15:35:08ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392019-01-0126372873510.17559/TV-20190110163709A Method of Mining Key Accounts from Internet Pyramid Selling DataJianying Xiong0Jiangxi Police College, Department of Security Technology, Nanchang, China 330013Internet pyramid selling causes great harm and has difficulty in obtaining evidence. As most of internet pyramid selling often uses virtual coins to complete cash settlement, criminals can exchange virtual coins by controlling visual accounts to transfer funds illegally. The purpose of the work is to mine the key accounts and build evidence chain of personnel and funds. In order to deal with a large number of transaction data, we characterize the virtual currency trading data, which is divided into seven transaction characteristics. The key accounts are these outliers which have too prominent trading behaviour. So we use positive samples to train One Class Support Vector Machine (OCSVM) classification to find trading behaviours’ boundary and detect outliers in classification. Then, the correlation between member’s information and pyramid selling hierarchical relationship can also be obtained by further linking the trading behaviour of key accounts. Finally, we can screen out the virtual accounts directly controlled by the pyramid selling organization, analyse the flow of funds, investigate and obtain evidence on amount of money involved in pyramid selling. The experimental results show that the classification model cannot only establish the normal model for account transfer behaviour, but also effectively identify abnormal transfer behaviour, thus improving efficiency of investigation and evidence collection for economic investigation departments.https://hrcak.srce.hr/file/322637abnormal transactionelectronic forensicsInternet Pyramid SellingOCSVM classification |
| spellingShingle | Jianying Xiong A Method of Mining Key Accounts from Internet Pyramid Selling Data Tehnički Vjesnik abnormal transaction electronic forensics Internet Pyramid Selling OCSVM classification |
| title | A Method of Mining Key Accounts from Internet Pyramid Selling Data |
| title_full | A Method of Mining Key Accounts from Internet Pyramid Selling Data |
| title_fullStr | A Method of Mining Key Accounts from Internet Pyramid Selling Data |
| title_full_unstemmed | A Method of Mining Key Accounts from Internet Pyramid Selling Data |
| title_short | A Method of Mining Key Accounts from Internet Pyramid Selling Data |
| title_sort | method of mining key accounts from internet pyramid selling data |
| topic | abnormal transaction electronic forensics Internet Pyramid Selling OCSVM classification |
| url | https://hrcak.srce.hr/file/322637 |
| work_keys_str_mv | AT jianyingxiong amethodofminingkeyaccountsfrominternetpyramidsellingdata AT jianyingxiong methodofminingkeyaccountsfrominternetpyramidsellingdata |