# On Evaluating IoT Data Trust via Machine Learning
## 5 Evaluation
### Summary of Results  
We evaluate the accuracy of the considered ML-based IoT data trust classification approaches via ten-fold cross-validation and present the results in Figure 5 for both the correlation and DST features (Section 4) extracted from the Intel Lab dataset that is augmented using both the RWI and Drift data synthesis methods (Section 3). We calculate the accuracy as the total number of correct classifications over the total number of classification inferences for ten datasets containing independently generated synthetic untrustworthy data. In addition, we calculate the standard deviation of the accuracy over the ten independently synthesized datasets for each case and present them as error bars in Figure 5.  
In Figure 6, we assess the ability of the trained models to generalize to data not seen during training. To this end, we provide the classification accuracy results for when the considered approaches are trained on a dataset augmented via either the RWI or Drift data synthesis method and tested on a dataset augmented via the other data synthesis method. We present the results for both the correlation and DST features.  
As seen in Figures 5 and 6, the correlation features yield higher accuracy compared to the DST features in all examined cases. Unsurprisingly, the supervised ML-based approaches (tagged as SVM and MLP) generally deliver the highest accuracy. The semi  
Figure 5: Data trust classification accuracy of the considered ML-based approaches trained and evaluated on both correlation and DST feature sets that are extracted from the Intel Lab dataset containing untrustworthy data synthesized using the RWI or Drift methods. The results are averaged over ten independent random realizations of data synthesis.  
Figure 6: Data trust classification accuracy of the considered ML-based approaches when evaluated on unseen data, i.e., trained on a dataset containing untrustworthy data that are synthesized using either RWI or the Drift method and tested on a dataset containing untrustworthy data synthesized using the other method.  
supervised ML-based approach utilizing the label propagation algorithm (tagged as propagation) performs close to SVM and MLP, especially when using the correlation features and data synthesized using RWI. The unsupervised ML-based approaches (tagged as \(k\)-means, SVM via \(k\)-means, and GMM) have significantly lower accuracy compared with the supervised ML-based approaches. The standard deviation of the accuracy values due to randomized data synthesis is relatively low, with the largest value being 1.2% for SVM using the correlation features and the RWI data synthesis method.  
It is evident from Figure 6 that the correlation features offer better generalizability compared to the DST features, in terms of the cross-dataset data trustworthiness classification accuracy. In addition, when using the correlation features, training data trust classification models on datasets augmented by RWI and evaluating them on datasets augmented by Drift results in higher accuracy for all considered ML-based approaches compared to the alternative, i.e., training on datasets augmented by Drift data and testing on datasets augmented by RWI. Furthermore, training on datasets containing RWI synthetic data while using the correlation features results in the highest cross-dataset accuracy for all considered ML-based approaches.