# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.4 IoT and Sensors  
An anomaly detection system for wastewater reuse was presented by [PERSON] et al. [83]. Hyperledger Fabric and multiple Machine Learning methods (polynomial regression, DBSCAN, autoencoders, and LSTM) were used to detect anomalies associated with water meter tampering. The blockchain is used to store all of the data obtained from the 2030 Wastewater Resources Group sensors that report data every hour. The data are then labelled as either anomalous or non-anomalous by each of the methods. [PERSON] et al. [84] used Reinforcement Learning to detect anomalies and faults in the smart grid. ITSA (Intelligent Time Series Anomaly detection) uses the CASAS (Center of Advanced Studies in Adaptive Systems) and OPSD (Open Power System Data) data sets injected with complex anomalous patterns to train the Reinforcement Learning models. An anomaly detection system for electricity consumption in smart grids was presented by [PERSON] et al. [86]. They use the k-Nearest neighbors algorithm with the combination of a data set collected from sensors. Their system is compared to DRAD (Distributed Real-Time Anomaly Detection in the networked industrial sensing systems) and ADSM (Anomaly Detection using Smart Meter data in the smart grid) and shows their successful detection rate regarding the anomaly occurrence rate. LSTM-based privacy preserving framework for smart power networks was proposed by [PERSON] et al. [87]. They used two data sets: ICS Power Systems and UNSW-NB15 for evaluation purposes. They presented the accuracy vs. loss graphs and the accuracy after the application of their privacy preservation for both data sets. A platform that manages crop growth and monitors crop diseases with the use of Blockchain technology and machine learning was developed by [PERSON] et al. [89]. The data set used for the training of the transfer learning model was the plant disease dataset. They presented the evaluation of their model using training and validation accuracy. [PERSON] et al. [90] proposed a Deep Learning based intrusion detection system for the IoT. They used the NSL-KDD data set, that contains different attack scenarios and classes. They evaluated the proposed system by using multiple settings (optimiser, init_mode, activation function), and presented the results through accuracy, average precision, average recall, and average F1-score. Ethereum blockchain was used by [PERSON] et al. [93] to create an SVM based intrusion detection system for the IoT. The Bot-IoT data set was used to test the performance of the system given two scenarios: one using 10 features, and the other using 34 features. The results are presented with the ROC curves, accuracy, precision, recall, F1-score, and fall out. [PERSON] et al. [95] created an intrusion detection framework for the protection of IoT and cloud networks. The intrusion is detected by using the Bidirectional Long Short-Term Memory (BiLSTM) algorithm. They evaluated the framework by using the BoT-IoT and UNSW-NB15 data sets. The results were presented using the accuracy, training times, and testing times. Additionally, they compared the results with other Machine Learning techniques (SVM, RF, NB, MLO) by using both data sets. [PERSON] et al. [94] created an IoT Botnet detection system based on the integration of static and dynamic vector features. Their data set consisted of both botnet and benign samples. Feature Extraction was used to reduce the data dimensions. The data were also standardised before they were considered to be ready for training and testing. The evaluation criteria used to provide the results of their experiment were: accuracy, precision, and F1. Additionally, they provided an ROC curve of the classifiers, PSI graph, and SCG. [PERSON] et al. [96] proposed a trust zone measurement architecture for blockchain based IoT systems. Their data set is comprised of various sensors in an IoT network taken from