## 5 Experimental Results
### Results and Discussion
#### 5.3.4 Accuracy Evaluation of Our One-Class Classifier  
We conducted two experiments to assess the performance of the proposed classifier through its two stages. The first experiment involved evaluating the training process of the autoencoder. In the second experiment, subsequent to the autoencoder's training, we assessed the accuracy of the one-class detector, which integrates both the autoencoder and the OC-SVM.  
Figure 10: MAAPE vs. used transmission approach for LSTM+transformer of global-based predictor.  
Figure 11: _MAAPE_ vs. used transmission approach for LSTM of cluster-based predictor.  
Figure 12: _MAAPE_ vs. used transmission approach for transformer of cluster-based predictor.  
**Experiment 1.** In this experiment, we train the autoencoder on the errors obtained from the predictor, which represent the difference between the predicted reading and the actual true readings. Figure 14 shows training and validation loss across the number of epochs. The observed decrement in training loss signifies the effective optimization of and successful reduction in the model's loss on the training error data. Concurrently, the reduction in validation loss suggests satisfactory generalization capability to unseen error data. The stabilization of both loss curves at a consistent value implies convergence and suggests that the training process has achieved an optimal state.  
Figure 14: Loss of autoencoder vs. number of epochs.  
Figure 13: _MAAPE_ vs. used transmission approach for LSTM + transformer of cluster-based predictor.  
**Experiment 2.** In this experiment, we compare our proposed approach with training an OC-SVM directly on the predictor's errors. To measure accuracy, we use a cyberattack function to generate low consumption data [11]. These malicious data, \(E_{m}\), are used only during evaluation to assess the performance of the one-class classifier and are not used during training. The malicious data constitute 50% of the evaluation set and are obtained as follows:  
\[E_{m}(d,t)=\alpha E(d,t), \tag{4}\]  
where \(E(d,t)\) denotes the benign consumption readings of day \(d\) and time period \(t\), and \(\alpha\) represents a constant scaling factor ranging from 0 to 1. The remaining 50% of the evaluation set consists of benign data from honest consumers, \(E(d,t)\). Table 5 gives the confusion matrix of two architectures--OC-SVM and the proposed combined architecture of the autoencoder and OC-SVM--while Table 6 compares the performance metrics. The results demonstrate a marked improvement in terms of accuracy, precision, recall, and F1 score with the proposed approach, which involves training an autoencoder on the predictor errors and subsequently using the encoded features to train the OC-SVM, compared to directly training an OC-SVM on the predictor errors. This improvement can be attributed to the autoencoder's ability to effectively capture and compress relevant features from the predictor errors, thereby reducing noise and dimensionality. This facilitates the OC-SVM's classification process by providing more representative and distinguishable features, which enhances the model's ability to accurately classify the data.