# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.3 Data Processing  
Social media behaviour detection is the focus of the study published by [PERSON] et al. [71]. They trained the Isolation Forest algorithm on user login data from Carnegie Mellon University and focused mostly on the user's login time. Principal Component Analysis (PCA) was used to process the data and obtain the outliers. [PERSON] et al. [72] proposed a system that creates an address classification. They used a data set with 26,313 addresses to perform this classification. The transaction history was pruned, and only the relevant (direct) transactions of an address were added to the transaction history summary. They tested multiple machine learning methods, and their tests show that LightGBM offers the best results. A confusion matrix was used for data representation. [PERSON] et al. [73] performed an identification of Bitcoin addresses by using a voting based classification method. Their goal was to detect which addresses are used by darknet market operators. To create a data set they extracted addresses, related and non-related to the darknet market, from multiple forums and sites. They expanded the collected addresses with the use of the address clustering heuristic, and compared the voting and non-voting identification methods. Data were presented by calculating the recall, precision, and F1 score of the results. [PERSON] et al. [75] proposed a detection model from Bitcoin address de-anonymising. They used a labelled data set, specifically: a five categories bitcoin address data set. Feature vectors were extracted from the historical transactions with using a parser. The tested algorithms were Logistic Regression, LightGBM, BAGC, CP, and AWAP. Their results were presented with the calculation of the accuracy, precision, F1 score, Jaccard, and NMI. The results were, additionally, summarised in four graphs. [PERSON] et al. [74] performed an Ethereum behaviour analysis using multiple Machine Learning algorithms (Logistic Regression, SVM, KNN, C4.5 Decision Tree, AdaBoost, and Random Forest). They gathered the data with NetFlow traffic and obtained real communication relationships between the nodes in the network. The obtained data were later analysed using the passive traffic association analysis method. Two sets of features were extracted from the training set: features based on the statistical information, and features based on the graph information. The node2 vec algorithm was also used for the graph representation. The precision rate and recall rate were used for the evaluation of the aforementioned machine learning algorithms. The results show that the best performance was given by the Random Forest algorithm. [PERSON] et al. [76] conducted performance analyses of machine learning methods on Bitcoin miners. They use multiple algorithms (LR, GB, RF, SVM, DNN, OCSVM, AE) and deployed them on real Bitcoin node implementations to test their training and testing latency. They used a data set for security detection, which consisted of normal and abnormal behavioural data. They concluded that the LR would be best for signature-based detection, but OC-SVM and AE would be best for anomaly detection. [PERSON] et al. [77] presented techniques and applications for crawling, ingesting and analysing data obtained from a blockchain. They used multiple machine learning methods to detect anomalies in the transaction behaviour, i.e., SVM, OCSVM, and K-Means clustering. The data were collected from websites that identify rogue accounts. Transactions for each account were clustered and the account features were extracted.