# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.3 Data Processing
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** \\ \hline Isolation Forest & [71] & & & & & \\ LightGBM & [72] & & & & & \\ Random Forest & [73] & [74] & & & & \\ AWAP & [75] & & & & & \\ OCSVM & & & [76] & [77] & & \\ AutoEncoder & & & & [77] & & \\ KMC & & & & [78] & & \\ Deep Learning & & & & [79] & & \\ Temporal Graph & & & & [79] & & \\ CML & & & & & [80] & \\ VGAE & & & & & [81] & \\ Neural Network & & & & & [82] \\ XGBoost & & & & & [82] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Anomaly detection in data processing. [PERSON] et al. [78] presented a one-class graph deep learning framework for anomaly detection on the Ethereum blockchain. To create a data set, they collected the external transactions from the Ethereum blockchain, marked the anomalies manually, and extracted the needed features. They evaluated their system by comparing the results to the OCSVM and Isolation Forest models. The results were presented by showing the accuracy and F1-score for each of them. [PERSON] et al. [79] made a temporal analysis for the Ethereum blockchain using Temporal Graph algorithms. To construct a data set, they extracted relevant data from the ethereum_blockchain data set that can be found in the BigqueryPublicData Repository. In addition to presenting the accuracy of the Random Forest and Logistic Regression, they also provided a visualisation of the temporal evaluation of the collected data. [PERSON] et al. [80] used Cascading Machine Learning to detect changes in entity behavioural patterns. The used two data sets: one from WalletExplorer, and the other contained Bitcoin data downloaded from the mainnet (from the last 3 years). The data set was cleared of unneeded types of transactions (lending), unlabelled and unusable data. The F1-scores were computed using k-fold cross-testing, and were shown together with their standard deviation. Multiple graphs were presented to show the different F1-score (bar charts) with regards to the batch size. Radar graphs show the precision, recall, F1-score, and number of samples for each batch size. Heatmaps show the F1-score of each test per batch size. A variational graph autoencoder was used by [PERSON] et al. [81] for transaction clustering and embedding generation. For their analytic framework they collected data from the Bitcoin blockchain (full node) and stored it in multiple NoSQL databases for easier processing. They presented the self-organising map output and explainable clustering for the retrieved data. The evaluation was conducted on the graph autoencoder, structural deep network embedding, and variational graph autoencoder. The results show that VGAE had the best result for both ROC and average precision. [PERSON] et al. [82] presented BlockEval--a blockchain simulator where blocks are generated using Deep Learning techniques. Two methods were evaluated: Artificial Neural Networks and XGBoost. They were compared, and the performance of their models is presented in the form of their median transaction value, median fee, block size, and block count.