# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
### Fraud Detection
#### 5.2.2 Cryptojacking, Malware, and Security
The results were presented with an accuracy and false alarm rate, and compared to ADvISE, SVDD, and OC-SVM. [PERSON] and [PERSON] [151] used machine learning to measure privacy protection and cyber risks. Multiple machine learning algorithms, i.e., XGBoost, Nearest Neighbor, SVM, and Decision-Tree were used to detect fraudulent behaviour. The data set used for testing purposes was collected from an insurance company. The detection was focused on whether the claims were fraudulent or not. The performance was measured using accuracy, precision, recall, F1-score and training time. A supervised learning model that can be used to identify illegal activities in the bitcoin blockchain was created by [PERSON] [153] et al. The data set was collected from the VJTI Blockchain lab, and the raw data were converted to CSV files. The necessary features were extracted and multiple hash addresses (from a single entity) were grouped by using multi-input heuristic clustering. The experimental study of their approach was conducted comparing the performance of SVM, LogReg, XGBoost, Random Forest and their custom proposed model. The results were presented by calculating the precision, recall, and F score, and by multiple graphs showing the scalability, learning curves, and performance of each method. A method for the detection of intrusion and DoS attacks on E-voting systems was presented by [PERSON] et al. [145]. They used the UNSW-NB15 data set to train and test two SVM classifier models (Gaussian and Linear). The evaluation was made using accuracy, area under the curve, and prediction speed. A cryptojacking detection method using machine learning was presented by [PERSON] [143]. He tested KNN, Random Forest, Decision Trees, SVM, and Naive Bayes. The data set consisted of cache hits and misses, and the performance was presented using the models accuracy, precision, recall, and F1-score. The best F1 score was given by SVM. The T-distributed stochastic neighbour embedding was used by [PERSON] et al. [154] to detect malicious user activity on Ethereum. They used an existing data set, and extracted the ones that could be associated with malicious behaviour. Node clustering was employed to detect such behaviour. The performed work was presented using Eigenvector visualisation. Supervised machine learning was used by [PERSON] and [PERSON] [142] to detect fraudulent accounts on the Ethereum blockchain. Data were obtained from Etherscan.io, and the empty wallets were removed (the ones with no transactions). The evaluation included three machine learning classifiers (Random Forest, SVM, and XGBoost). The probability specificity, recall, precision, FPR, F1, and confusion matrices were presented for each of the evaluated methods. Random Forest obtained the best results. [PERSON] et al. [152] presented the detection of illicit accounts on the Ethereum blockchain by using XGBoost. They created the data set by collecting the data from the Etherscamdb and a local Geth client. They collected both normal accounts and those labelled as illicit. The data were filtered by removing the duplicate accounts, their transactions were gathered using Etherscan API, and removing unsuccessful transactions. The data were visualised utilising a 2D and 3D t-SNE scatter plot. To evaluate their proposal, they calculated the accuracy, sensitivity, specificity, F1-score, and AUC for multiple scenarios. They also provide a graph with the average logarithmic loss, classification error, and a confusion matrix. A method for the detection of suspicious users was proposed by [PERSON] and [PERSON] [136]. They used two data sets to evaluate their system: Bitcoin-OTC and Bitcoin-Alpha. Multiple machine learning techniques were evaluated, such as SVM, Naive Bayes, Decision Tree, and Neural Networks. They presented the results of the evaluation providing the precision, recall, F1-score, support, and accuracy from each machine learning algorithm, and for each data set. A supervised learning model to identify illegal activities within the bitcoin blockchain was presented by [PERSON] et al.