# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
### Fraud Detection
#### 5.2.1 Financial Fraud Detection
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline Method/Application & & & & & & \\ Method/Application & & & & & & \\ \hline LSTM & [108] & & & & & \\ Multi-layer Perceptron & [119] & & & & & \\ Graph2 Vec & [111] & & & & & \\ GCN & [112] & & & & \\ AutoEncoder & [112] & & & & & \\ AdaBoost & [112, 113] & & & & \\ CNN & & [114] & [115] & & & \\ Node2 Vec & & [116] & & & & \\ Ordered Boosting & & [117] & & & & \\ XGBoost & & & [118] & & & \\ Random Forest & & & [119, 120] & & & \\ KNN & & & & & & \\ Federated Learning & & & & & & \\ SVC & & & & & & \\ Logistic Regression & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Financial fraud detection. a data set, they collected Ponzi and non-Ponzi scheme contracts from multiple websites. The contracts were converted from bytecode to opcode using the pyevmasm library and removed the operands. The opcodes were additionally converted to eigenvectors (using Bag Of Words - BOW) to conduct feature extraction utilising n-grams. BOW allows the definition of stop words, so that frequent operators can be removed from the opcode. They compared their method to multiple others by presenting the precision, recall, and F-score. Machine learning was used by [PERSON] et al. [120] to detect Ponzi schemes on the Ethereum blockchain. To create a data set, they collected smart contract source code from etherscan.io. and checked whether they were Ponzi scheme contracts manually. The features were then extracted without the course code, all the related transactions were collected and unsuccessful transactions were removed. Next, the contracts were converted from bytecode to opcode, the features were classified, and feature extraction was performed. Multiple algorithms were evaluated and combined, and their performance was presented using precision, recall and F-scores. [PERSON] et al. [118] used XGBoost to detect Ponzi schemes on the Ethereum blockchain. To test their system, they collected smart contracts from etherscan.io. The bytecodes were converted to opcodes and their frequency calculated. The contracts were labelled as Ponzi or non-Ponzi. The results were presented by calculating the precision, recall, and F-score. Machine learning methods were used by [PERSON] et al. [119] to detect Ponzi schemes on the bitcoin blockchain. To create a data set they collected bitcoin addresses related to Ponzi schemes and their respected transactions. They extracted features that could be useful to detect Ponzi schemes. Additionally, the data set was also filled with a number of addresses not connected to Ponzi schemes. To create an evaluation, they selected several machine learning classifiers: RIPPER, Bayes Network, and Random Forest. They calculated their accuracy, specificity, sensitivity, precision, F-measure, G-mean, and AUC. The results were visualised using confusion matrices. [PERSON] et al. [125] proposed the detection of money laundering with Ethereum cryptocurrency transactions. To create a data set they collected wallets from etherscan.io and extracted the wallets with the largest trading volumes. For the minimisation of data, they chose the expectation maximisation algorithm, and the k-means algorithm for the clustering and weight defining. To present the results they calculated the accuracy, precision, F-measure, and True Negative Rate. A ROC curve and Precision Recall Curves were used for visualisation purposes. A federated learning framework was used by [PERSON] et al. [126] to detect poisoning attacks. For the evaluation they used the MNIST and CIFAR-10 data sets. The performance of their model was presented by calculating accuracy for different numbers of participants, and the percentage of modified labels that indicate the strength of the poisoning attack. [PERSON] et al. [110] used machine learning classifiers to detect scams within a bitcoin blockchain. They searched for bitcoin generator scams with multiple search engines: Google, Bitcoin.fr, CuteStat.com, and the Internet Archive. They included multiple classifiers for evaluation purposes.