# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
### Fraud Detection
#### 5.2.4 IoT and Sensors  
[PERSON] et al. [160] proposed a multiple object tracking system using HashNet from deep hash extraction. They used the MOT15 data set for the evaluation and acquired multiple results (mostly tracked agents, mostly lost agents, False Positives, False Negatives, identity switches, multi-object tracking accuracy and multi-object tracking precision).  
AIT is an deep learning based trust management system for vehicular networks proposed by [PERSON] et al. [161]. To create a data set, they used SUMO (Simulator of Urban MObility) to generate maps and vehicular network simulations. Their model is based on the Feedforward Neural Network, and for the evaluation (precision, recall, percentage of malicious nodes, and accuracy) it was compared to the Recurrent Neural Network and Convolutional Neural Network.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method/Application** & & & \\ \hline LightGBM & [155] & & \\ Deep Learning & [156] & & \\ Random Forest & & & \\ Graph Embedding & & & \\ Linear Regression & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Data processing.  
[PERSON] et al. [163] used blockchain and Federated Learning for intrusion detection in vehicular edge computing. They used the KDD Cup99 data sets of edge vehicles to test the proposed system and represent the precision rate, recall rate, and accuracy rate changes with respect to data size.  
[PERSON] et al. [162] proposed a target detection and automatic monitor scheme based on blockchain and deep learning models. They used the CIFAR-10 and Mnist data set to conduct the performance evaluation of the proposed model. The results showed the training accuracy and loss.  
[PERSON] et al. [164] used Generative Adversarial Neural Networks to detect fraudulent behaviour in the IoT. They prepared two sets of data: one set for the digital signature frauds (containing messages, private keys, and public keys), and another data set for asymmetric encryption frauds (plaintext, private keys, and public keys).  
Supervised machine learning for outlier detection was used by [PERSON] et al. [165]. They created a simulation of an IoT network with 100 sensors and collected their data. The performance was presented using fault tolerance and accuracy.  
BITS: A blockchain based intelligent transportation system was proposed by [PERSON] et al. [166]. They used machine learning to detect outliers within the system. Simulated data were used from multiple data and randomly injected 10% outlier values. They presented the outcome of the Isolation Forest model using a graph that included the accuracy and false positive rate.  
A multi-level trust mechanism against Sybil attacks in vehicular networks was presented by [PERSON] et al. [167]. They tested the system with three different machine learning algorithms: SVM, KNN, and Random Forest. The algorithms were tested using the VeReMi data set that contains multiple types of attacks: Constant attack, Constant offset attack, Random attack, Random offset attack, and Eventual stop attack. They presented the accuracy and time consumed per test for each of the selected algorithms, and showed that KNN gave the best ratio of accuracy and consumed time.  
[PERSON] et al. [168] presented a system for fraud detection and risk measurement in the Insurance sector. For their experiment they used four machine learning classifiers (Decision Tree, SVM, Nearest Neighbor, and XGBoost) on a data set obtained from an insurance company. They calculated the accuracy, recall, precision, and F1-score, and showed that XGBoost performed the best. Additionally, they provided the normalised confusion matrix for XGBoost.