# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.1 Financial Anomaly Detection  
One of the situations where detecting anomalies can be of use is money laundering. In their research, [PERSON] et al. [39] performed a comparative analysis of the use of multiple machine learning methods to detect money laundering in the Bitcoin blockchain. For the experiment, they used the elliptic data set (from which they excluded the time-step) and the aggregation features to enhance the performance. From the whole data set, unknown labels were excluded and only the licit and illicit remained. They used the Receiver Operating Characteristics (ROC) curve to present the visualisation of each of the tested techniques: Ensemble Learning, Random Forest, Extra Trees, Bagging, AdaBoost, Gradient Boosting, and k-Nearest Neighbours. Graph Convolutional Networks assisted by linear layers were also used by [PERSON] et al. [40] to detect money laundering in the Bitcoin blockchain. The Elliptic data set was used for evaluation, and their method was then compared to the original GCN and Skip-GCN. The results that included precision, recall, F1 score, and accuracy, showed that their proposal reached the best score. [PERSON] et al. [41] tackled the problem of under-price DoS attacks in the Ethereum blockchain network. In under-price DoS attacks, malicious users perform denial of service attacks in order to exploit flaws in blockchain networks. One example of such exploits includes the Ethereum fee mechanism. In this scenario, the attackers pay a small fee for a large number of transactions. To test their network they created 2000 accounts and tested it with waves of normal transaction flows (2000 transactions), under-price DoS attack (10% of the transactions were malicious), and the Ethereum Boom (transactions from 20 December 2017). To test the flows they used real transactions from 5 May 2019. The results of the Machine Learning models show that the Decision Tree and Random Forest methods are most suitable for this type of task. A system for credit card fraud prevention was presented by [PERSON] et al. [43]. The system is trained to recognise anomalies within transactions stored on a blockchain. There were three data sets used to perform the tests: the credit card fraud detection data set (ULB), synthetic financials data set, and the German credit card fraud data set. Four Machine Learning algorithms were tested (Logistic Regression, SVM, XGBoost, and Random Forest), and the results showed that (based on the True Positive rate), the Random Forest algorithm had the best accuracy and Kappa value. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline Ensemble Learning & [39] & & & & & \\ GCN & [40] & & & & & \\ Decision Tree & [41] & & & & [42] & \\ Random Forest & [41] & [43] & [44] & & & [45] \\ XGBoost & & & & [46] & & [47] \\ Isolation Forest & & & & & [48] & \\ LSTM & & & & & [49] & \\ LightGBM & & & & & [50] & \\ OCSVM & & & & & [51] \\ Deep AutoEncoder & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Financial Anomaly Detection. Cryptocurrency \"pump and dump\" schemes are a way of exploiting the blockchain market, where one first buys a cryptocurrency at a low price. Then, with the help of social media and similar platforms, they convince other investors to purchase the cryptocurrency, and, thus, increase its value. As others invest, the price of the cryptocurrency rises, and the organisers sell their shares at an (often) much higher price. [PERSON] and [PERSON] [46] presented a cryptocurrency pump and dump detection scheme. Quantification and detection is done based on data obtained from the Binance Exchange. Data were collected for the data set from 172 cryptocurrencies in one-second intervals, and filtered to contain only certain fields relevant for this analysis (timestamp, price of last trade, 24 h trading volume, and 24 h trade count). Also, 18 telegram channels were monitored to obtain timestamped messages associated with cryptocurrency pump and dump schemes.