# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.2 Cryptojacking, Malware, and Security  
[PERSON] et al. [52] created BlockFLA, an accountable federated learning framework based on the Hyperledger Fabric blockchain. The main goal of their work is to detect backdoor attacks. BlockFLA's performance was tested by using trojan patterns on the CIFAR10 data set. An agent who corrupts the data set is considered an adversary. They presented their results by sampling from a Dirichlet distribution. ContractWard [53] is a system that detects vulnerabilities within Ethereum smart contracts. They use a combination of the Synthetic Minority Oversampling Technique (SMOTE) and TomekLinks to deal with the class-imbalance problem in the training sets. The classification was conducted using five supervised learning techniques (eXtreme Gradient Boosting, Adaptive Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor). Micro-F1 and Macro-F1 are used to present the measurements of the conducted tests. ALICIA, applied intelligence in the blockchain-based VANET, was presented by [PERSON] et al. [55]. They designed the system to provide vulnerability detection using \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** & **Method/Application** \\ \hline Federated Learning & [52] & & & & & & \\ XGBoost & [53] & & & & & & \\ Neural Network & [55] & & [56] & & & & \\ Deep Learning & [57] & & & & & [58] \\ Naive Bayes & & & [59] & & & & [61] \\ LSTM & & & & [56] & [62] & & \\ SVM & & & & & & \\ Decision Tree & & & & & & & \\ Random Forest & [63] & [64] & [56] & & & & \\ KNN & & & & & & & \\ Temporal Graph & & & & & & & \\ OCSVM & & & & & & [67] & [68] \\ Deep AutoEncoder & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Security. neural networks. An existing data set was used that contained car trajectories and vehicle telemetry (such as speed, acceleration, heading, etc.). The data set was additionally supplemented with simulated accident events. The results of their evaluation are shown on a graph containing the relationship between accuracy and the false positive rate. [PERSON] et al. [57] presented Eth2 Vec--a deep learning vulnerability detection system for Ethereum smart contracts. To evaluate their proposed system, they compared it to SVM. They collected smart contracts from Etherscan.io to create the data set needed for the evaluation. The evaluation was presented using precision, recall, and an F1-score for each, and it can be observed that Eth2 Vec offers a better average performance. Another vulnerability detection model for Ethereum smart contracts was presented by [PERSON] et al. [63]. To create a data set they collected source codes of smart contracts from the official Ethereum website, which were then labelled using Oyente. The feature extraction was preformed using the n-gram algorithm. To reduce the dimension of the data set, the opcodes were simplified by removing operands and classifying similar opcodes. The test was conducted on Random Forest, SVM, and KNN. The results are presented by calculating the F1-score, Micro-F1, and Macro-F1. The ROC curves of their models were presented additionally. Cryptojacking is the process of adding covert malware that performs cryptocurrency mining on one's computer. The attackers use the victims' resources to collect mining rewards [59,60]. [PERSON] et al. [60] proposed an approach to detect cryptojacking using Recurrent Neural Networks (LSTM). This approach uses the browser header data to detect malicious behaviour. The collected data are pre-proccesed so that the function features, suspicious data, and the function calling sequence are extracted and categorised (replaced by predetermined symbols). The evaluation results are shown, presenting the precision, recall, and F1-score with the addition of the hardware performance test results.