# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
### Fraud Detection
#### 5.2.1 Financial Fraud Detection
The results were presented by calculating precision, recall, and the F1 score. It shows that SVC and MLP provided the best performance. [PERSON] et al. [127] presented a comparative study of machine learning algorithms used for fraud detection in blockchain networks. They used the node2 vec algorithm to collect data for the data set. Features were then extracted from the collected data and stored in a CSV file. The CSV was then converted into a dictionary using the node2 vec algorithm. A network edge list file was created and the embedding dimensionality reduced. Additionally, the features had to be normalised (the value 1 was assigned to fraudulent transactions, and 0 for the others), the mean and standard deviation were calculated. The results are shown by the achieved accuracy of each algorithm, and it was observed that logistic regression performed the best. A security enhancement to financial transactions in the bitcoin blockchain was offered by [PERSON] and [PERSON] [121] using machine learning. They used the Elliptic data set and the k-means clustering technique to partition unlabelled data. The measurementswere made by using four machine learning algorithms: the Naive Bayes, Bayes Network, AdaBoost, and Random Forest. The precision, recall, TP rate, FP rate, PRC, and area under the ROC curve were calculated. The results showed that Random Forest had the best performance out of the selected algorithms. [PERSON] et al. [122] used machine learning to detect illegal transactions on the bitcoin blockchain. They collected hash lists of legal and illegal transactions from multiple websites (such as Silk Road and Blockchain Explorer) to create their data set. The testing was conducted on the artificial neural network and random forest classifier. The F1-scores of these two methods show that random forest was a better fit for this type of detection. [PERSON] et al. [113] proposed a framework used for the detection of phishing scams on the Ethereum blockchain. They collected data from Etherscan and added three filer rules to remove accounts with low activity levels, i.e., removing the smart contracts accounts, removing accounts with less than 5 transactions and transfer-in transactions with less than four, and removing accounts whose greatest balance was less than five. The testing was conducted on multiple Machine Learning models including SVM, KNN, and AdaBoost. For each model the precision, recall, F1-score and AUC were presented, and it was concluded that AdaBoost performed best. A novel methodology for the detection of high yield investment programmes Bitcoin addresses was proposed by [PERSON] et al. [123]. The data were collected by searching for HYIP addresses and collecting their transactions. Feature extraction was then conducted, unneeded parts of the transaction were removed, the BTC was converted to USD, and the transactions were labelled as spent, received, or Coinbase. The evaluation was conducted on multiple algorithms (RF, XGBoost, Neural Network, SVM, k-NN) and the results were shown as TPR and FPR. The best result was provided by Random Forest. [PERSON] et al. [124] used the Random Forest classifier to create a detector for eclipse attacks for the Ethereum blockchain. Eclipse attacks are used to isolate a certain user from a network by controlling their outgoing connections. In order to collect data for the data set, they collected the UDP packets from normal and unsolicited nodes. The data were then converted into a readable format using the Ethereum UDP packet dissector and added into the data set. They evaluated their proposition and presented the results for the Random Forest classifier in the form of its precision, recall, F-score, and support.