Table 13.
Research on abnormal cryptocurrency trading (2)
| Models | Data | Results | Innovation | References |
|---|---|---|---|---|
| RF, MLP, LGBM | 9841 lines of known fraudulent and valid transactions through Ethereum | LGBM algorithm shows slightly better performance in the specified dataset scenario up to 98.60% accuracy up to 99.03% after parameter optimization | This paper proposes an LGBM-based transaction fraud detection method, along with other algorithms such as RF and MLP, to classify Ethereum fraud detection datasets with limited attributes | Aziz et al.181 |
| DNN, Kmeans, Decision Tree, RF |
Real Ethereum transaction dataset from 2017 to 2019 | The model achieved 97.72% accuracy in Ethereum attack detection and 99.4% accuracy in attack classification | Deep neural networks are used in a two-stage deep learning-based Ethereum threat search model to detect attacks, and supervised and unsupervised techniques are combined to classify attacks. | Rabieinejad et al.182 |
| SVM, CNN, RCNN, GCN, GAT, S_HGTNs |
There were 1251 normal contracts and 131 fraudulent contracts on Ethereum | The proposed model outperforms the conventional model in the classification results with a low standard deviation, demonstrating the model’s validity and stability. | A heterogeneous graph variant network (S_HGTNs) suitable for smart contract anomaly detection is constructed to detect financial fraud on Ethereum platform | Liu et al.183 |
| LION (Lightweight and Identifier-Oblivious Engine) |
Bitcoin P2P security research provides information | The detection accuracy of the model for attack prototypes and real-world anomalies exceeds 97% F1-score. | Build the LION model for anomaly detection in the P2P network of cryptocurrency blockchain, and conduct data-driven research and evaluation on LION | Fan et al.184 |
| LSTM | Complete trading data for the 5 stable top 10 exchanges on CoinMarketCap | The test results indicated that some abnormal transaction amounts were related to policy changes and industry events, while other abnormal transaction amounts were suspected to be related to illegal acts. | By examining the relationship between the quantity of each transaction and other transactional information, it was possible to identify the significance of various transactional elements. From a time series forecasting standpoint, LSTM is utilized to examine irregular transactions. |
Gu et al.185 |
| Model Review | The ideas of public blockchain and joint blockchain, as well as the current methods for identifying anomalous activity, are explained in depth. | The existing mainstream blockchain security-related datasets are summarized and analyzed to provide reference for the research of blockchain security awareness. | Yan et al.186 |