A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection
The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not us...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/1/147 |
| _version_ | 1811302021767102464 |
|---|---|
| author | Blaž Podgorelec Muhamed Turkanović Sašo Karakatič |
| author_facet | Blaž Podgorelec Muhamed Turkanović Sašo Karakatič |
| author_sort | Blaž Podgorelec |
| collection | DOAJ |
| description | The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions. |
| first_indexed | 2024-04-13T07:19:10Z |
| format | Article |
| id | doaj.art-95f35bcd54b0409aa205535e38aae263 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:19:10Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-95f35bcd54b0409aa205535e38aae2632022-12-22T02:56:39ZengMDPI AGSensors1424-82202019-12-0120114710.3390/s20010147s20010147A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly DetectionBlaž Podgorelec0Muhamed Turkanović1Sašo Karakatič2Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaThe basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.https://www.mdpi.com/1424-8220/20/1/147blockchaintransactionsdigital identity managementanomaly detectionmachine learning |
| spellingShingle | Blaž Podgorelec Muhamed Turkanović Sašo Karakatič A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection Sensors blockchain transactions digital identity management anomaly detection machine learning |
| title | A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection |
| title_full | A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection |
| title_fullStr | A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection |
| title_full_unstemmed | A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection |
| title_short | A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection |
| title_sort | machine learning based method for automated blockchain transaction signing including personalized anomaly detection |
| topic | blockchain transactions digital identity management anomaly detection machine learning |
| url | https://www.mdpi.com/1424-8220/20/1/147 |
| work_keys_str_mv | AT blazpodgorelec amachinelearningbasedmethodforautomatedblockchaintransactionsigningincludingpersonalizedanomalydetection AT muhamedturkanovic amachinelearningbasedmethodforautomatedblockchaintransactionsigningincludingpersonalizedanomalydetection AT sasokarakatic amachinelearningbasedmethodforautomatedblockchaintransactionsigningincludingpersonalizedanomalydetection AT blazpodgorelec machinelearningbasedmethodforautomatedblockchaintransactionsigningincludingpersonalizedanomalydetection AT muhamedturkanovic machinelearningbasedmethodforautomatedblockchaintransactionsigningincludingpersonalizedanomalydetection AT sasokarakatic machinelearningbasedmethodforautomatedblockchaintransactionsigningincludingpersonalizedanomalydetection |