# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
### Fraud Detection
#### 5.2.1 Financial Fraud Detection  
KaRuNa is a blockchain-based framework for fraud cryptocurrency schemes created by [PERSON] et al. [108]. Their model was based on the LSTM classifier. They used the Elliptic data set to evaluate the performance. This data set was enhanced by adding a classification score for the reduced raw cryptocurrency data obtained from social media, newsapi, and other web sources. The results are visualised with graphs depicting the analysis of tweets and a fraud scheme classification confusion matrix. The precision, recall, and F-score were also provided. A multilayer perceptron architecture to detect cryptocurrency deception was presented by [PERSON] and [PERSON] [109]. The data set for their evaluation was collected from the CMC website and labelled either legitimate or deceptive. The evaluation was conducted using Linear Regression, Softmax Regression, SVM, and MLP. The accuracy, precision, TPR, FNR, TNR, and FPR were presented, and it is observed that MLP performed the best. An improved graph classification algorithm (Graph2 Vec) for phishing detection on the Ethereum blockchain was proposed by [PERSON] et al. [111]. To create a data set they gathered phishing addresses from etherscan.io and also added the same number of normal addresses. They gathered the transactions for every address, removed the redundant data, and also removed the addresses with less than 10 transactions and more 300 transactions. They presented the evaluation of their algorithm by calculating the precision, recall, and F1-score. They also compared the performance to several other methods, such as node2 vec, WL-kernel, and Graph2 Vec. A phishing scam detection system for Ethereum blockchain was presented by [PERSON] et al. [112]. They used a graph convolutional network and autoencoder to detect phishing accounts. As a data set they used the Ethereum transaction history. They provided a performance comparison of their GCN method, Deep Walk, Node2 Vec, and LINE. They showed the results of their AUC, recall, precision, and F1-score. [PERSON] et al. [114] proposed a financial fraud detection method using deep learning (a Convolutional Neural Network). They gathered the data from a large O2O supply chain management platform to create the data set, and calculated the precision, recall, and F1-score of the experimental evaluation. Additionally, they compared their proposition to SVM and a decision tree. [PERSON] et al. [116] proposed a financial fraud detection system by using Node2 vec. To evaluate their proposal they used a data set provided from an Internet financial service provider in China. They compared Node2 Vec, DeepWalk, and SVM, and presented their results by showing the calculated precision, recall, F1-score and F2-score. [PERSON] et al. [115] created an improved Convolutional Neural Network to detect Ponzi contracts. They obtained the data for the data set from etherscan.io. They collected the contracts and converted the hexadecimal bytecodes to the corresponding decimal number. Additionally, they standardised the bycodes. They performed the evaluation on their algorithm and several others (Decision Tree, SVM, XGBoost, OCSVM, Isolation Forest, Random Forest), and presented their corresponding precision, recall, and F-scores. A Ponzi scheme is not a novel fraud. It is an investment fraud where the scammer pays the old investment clients revenue by using the investments of new clients rather than through legitimate business actions. In a blockchain environment this is done by using smart contracts [120]. [PERSON] et al. [117] proposed a Ponzi scheme detection method. To create