# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 5 Review of Applications
#### 5.1.4 IoT and Sensors
The evaluation was conducted by using multiple data sets: the CICIDS2017 data set, a power system data set and a web robot (Bot)-Internet of Things (IoT) data set. They presented the results in the form of accuracy, false alarm rates, detection rates, training time and test time. They also compared their proposition with SVM, Random Forest, and Naive Bayes. [PERSON] et al. [104] provided a GNN anomaly detection method on industrial time-series logs. They used a data set provided by SWaT, and represented the accuracy of their system proposal. [PERSON] et al. [91] proposed a deep learning based collaborative anomaly intrusion detection system. They used the KDD CUP1999 data set (the iris, lymphographic, vehicle, and glass data sets), and compared their results to other similar propositions. They presented the accuracy, validation time, average filtration efficiency, TPR, FPR, and the relationship between attack intensity and average hiding probability. [PERSON] et al. [105] proposed a blockchain-based data collection and anomaly detection for the estimation of battery state-of-health. The proposed system was evaluated using battery charging data provided by NASA. The collected data were processed so that the units and magnitudes became normalised. The normalised data were enlarged 100 times and the collected data had to be in a range from 0 to 100. They use Isolation Forest to detect anomalies within the data, and showed the evaluation results using F1 and F2 scores. The results were, additionally, compared to several different algorithms, i.e., K-means, FCM, and PSO+FCM. [PERSON] et al. [92] presented an MS-DNN (Multi-Source Deep Neural Network) framework that detects anomalies within manufacturing systems. They validated the framework using two data sets: a factory automation data set and a SWaT (Secure Water Treatment) data set. The evaluation results are presented using precision, recall, F1, and accuracy. An LSTM based anomaly detection framework was proposed by [PERSON] et al. [88]. They used three data sets for evaluation purposes: the HDFS Benchmark data set, the HDFS data set and the oil industry data set. The performance of their model was evaluated presenting the accuracy, precision, recall and F1-score for each data set. SP2F, an SLSTM (Stacked Long-Short Term Memory) privacy-preserving framework for agricultural unmanned aerial vehicles was presented by [PERSON] et al. [103]. Two IoT data sets, ToN-IoT and IoT Botnet, were used for evaluation purposes. The results were compared to two scenarios, one before the two-level privacy was applied to the data sets and the other after it was applied to the data sets. The results were compared to the Random Forest, Decision Tree, and Naive Bayes, and represented with accuracy, detection rate, precision, F1-Score, execution time analysis, confusion matrices, and ROC curves. [PERSON] et al. [101] evaluated two different implementations that are used for model validation. One implementation is based on the chaincode, and the other is a combination of chaincode and an Oracle web service component. They used two data sets for the evaluation of their proposed system, a generated synthetic 2D data set and an EEG eye state data set. Since the EEG data set was smaller than the synthetic 2D data set, it was expanded by bootstrapping the original data. The data on both data sets were indexed to improve the evaluation speed. Their model was evaluated on both data sets, and the results presented with the model inference runtime, distribution of runtime, and the overall overheads. [PERSON] et al. [106] proposed GuardHealth, a data management and graph convolutional network enabled anomaly detection system for healthcare. They evaluated the proposal by simulating malicious and benign nodes. Their model was compared to logistic regression and multilayer perceptron, and showed the average trust value and precision.