# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.4 IoT and Sensors
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{Method/Application} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Method/Application** \\ **S** \\ **\end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **[83]** \\ **S** \\ **\end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **[85]** \\ **S** \\ **\end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **[88]** \\ **[90]** \\ **S** \\ **\end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\
\begin{tabular}{c} **[91]** \\ **[92]** \\ **B**ISTM** \\ **\end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **[95]** \\ **[96]** \\ **[97]** \\ **[100]** \\ **G**SM** \\ **G**N** \\ **G**N** \\ **G**N** \\ **G**N** \\ **Isolation Forest** \\ **GCN** \\ \hline \hline \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Anomaly Detection in IoT. the UCI Machine Learning Repository. This data set contains both malicious and benign data. The proposed work was tested, and they evaluated four machine learning algorithms: Autoencoder (their solution), Isolation Forest, SVM, and Local Outlier Factor. The results, presented by the accuracy and detection time, showed that their autoencoder method functioned best. [PERSON] et al. [98] proposed a system that uses Deep Learning and blockchains to enable security in the industrial IoT. They used the Bot-IoT data set that contains labelled data of smart devices and multiple test scenarios. The evaluation of the model was performed using recall, precision, overall accuracy, and average accuracy. [PERSON] [102] created a collaborative anomaly detection system that focuses on noisy sensor data. The proposed system has multiple clients, and each of them has an individual data set that is a result of repeated measurements. The data were processed so that the data set was split into three equal blocks and each variable was standardised. Additionally, outlying samples of data were removed from the data set. A variable-wise anomaly score was calculated for each sample. The results were presented using a simple line graph. [PERSON] et al. [85] presented SS-ITS--a secure scalable intelligent transportation system. The system gathers and performs Feature Extraction on urban traffic data. The collected data are divided into different windows and processed accordingly. They used the local outlier factor to identify anomalies and extract the training data. For experimental results they used two urban data sets: ECML PKDD 2015 and HUMBI. They compared the proposed solution to baseline anomaly detection solutions (DILOF and MSCRED), baseline blockchain learning solutions (DRL and DUeling DQL), and baseline high-performance computing solutions (LoTAD and FUAD). Deep learning and blockchain were used by [PERSON] et al. [99] for secure and private dashcam video sharing. Their data set contained 519 images and 1093 sounds. The evaluation results were presented by calculating the accuracy, F-measure, precision, and recall. Optimal thresholds, image and sound detection comparison graphs, and overhead comparisons are visualised additionally. [PERSON] et al. [97] created a chained anomaly detection model that uses deep learning (autoencoder). To evaluate their system, they used the CICIDS2017 data set. The data set contains network traffic information and common attacks. The evaluation was conducted on a real network where the data set was distributed among the nodes. They presented the results of the accuracy, loss, validation accuracy, and validation loss. They also presented the average epoch time comparison when using blockchain as a way of storing the weight updates and revised models, and when using the classical method of storage. The results show that when using blockchain, the latency is bigger. [PERSON] et al. [100] created DeepCoin, a deep learning (RNN) blockchain-based energy exchange framework.