# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.1 Financial Anomaly Detection
The data set was cleared of all small trade amounts, windows of 30 min were centered on ground-truth timestamps, and certain features (like entropy, stability, flat spots, etc.) were computed using the tsfeatures library. Finally XGBoost was used to detect pumps. Their timeline visualisation shows the comparison of pumps found by the model, and pumps from the ground truth. [PERSON] et al. [44] also created a system that detects pump and dump cryptocurrency manipulations by analysing social media and cryptocurrency market data. They collected the needed data from Telegram, Twitter, and CoinMarketCap.com. The data were then labelled as either pump/not-pump messages, and the Random Forest classifier was used to detect and predict pump and dump manipulations. Their approach was validated using an area under the receiver operating characteristic curve (ROC-AUC). A system that detects suspicious activities in financial transactions and distributed ledgers was created by [PERSON] et al. [42]. They conducted data preprocessing by first filtering out entries containing invalid values, then building a collection of vectors (grouped transactions by user) that will be analysed and filtered using the RFM (Recency, Frequency and Monetary) features. The Pearsons correlation coefficient was used to calculate the mutual influence of the features. Missing values from the data set were filled with a median value of that column, and the peak values were eliminated by subtracting the columns mean from it and then dividing it by its standard deviation. They used use cases to train decision trees, extract anomalies and detect anomalous accounts. They visualised their data using the t-SNE algorithm, as well as 2D and 3D scatter plots. An LSTM network for anomaly detection and classification of Ethereum smart contracts was presented by [PERSON] et al. [48]. They collected smart contracts from Ethereum, identified behaviour patterns manually, extracted features, and proposed a data slicing algorithm to slice the collected contracts. The proposed LSTM model was evaluated on the created data set, and the results were presented with the satisfactory precision, recall, and F-score. An anomaly detection model for Bitcoin transactions was presented by [PERSON] et al. [50]. The data used for the evaluation were obtained from the Bitcoin blockchain. They use One-Class SVM to detect outliers, and K-means clustering to gather similar attacks. The results of the evaluation were presented using confusion matrices, cluster frequencies, and detection results. [PERSON] et al. [47] presented a machine learning based method for blockchain transaction signing and personalised anomaly detection. The data were collected from the Ethereum public main network. Isolation Forest was used to detect anomalous transactions, while Random Forest was used to determine the feature importance. Honeypot contracts represent malicious contracts that are designed so that they have certain obvious flaws and attract other malicious users that wish to profit off of them. Of course, the flaws are carefully used to mask traps, and the only one that will ultimately profit is the creator of the honeypot contract [49]. [PERSON] et al. [49] created a system that detects these kinds of contracts. To collect the data needed for the data set, they extracted honeypot contracts from the HONEYBADGER project and analysed each of the entries whether it was a honeypot or not. The results were then categorised by the used technology,and the Ethereum ledger is downloaded into the data set. Feature extraction was conducted by converting the bytecode into opcodes, analysing the opcode frequency and using the bi-gram features to determine the opcode combination. Classification was done with the use of the LightGBM algorithm. The evaluation metrics used to present their results were precision, recall, AUC, and F1. A transaction pattern analysis was performed by [PERSON] et al. [45] to identify High Yield Investment Programmes (HYIP).