## 3 Results
### Binary Classification Result Scenario  
Noteworthy progress in IoHT cybersecurity is the binary deep learning-based IoHT attack detection model. This innovative model provides a highly accurate method for identifying cyber threats within the IoHT infrastructure. While it employs a binary classification paradigm, which is less detailed than its multiclass equivalent, it is equally vital in preserving security. The model's primary function is to verify the existence or absence of cyber threats within the IoHT system. Table 4 details that the model's recall metric is significant, boasting a perfect 100% for both classes. Recall is a critical measure of the model's capacity to identify all genuine positives within the dataset. This achievement is most important for IoHT security, as the model identifies all legitimate threats.  
The F1-Score of the model for both the 'No Attack' and 'Attack' classes achieved the optimal value of 100%. Since the F1-Score is a balanced average of precision and recall,  
Figure 3: Analysis of the confusion matrix multi-classification results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Accuracy & 0.998561 \\ Specificity & 0.996823 \\ Precision & 0.998569 \\ Recall & 0.998561 \\ F1-Score & 0.998563 \\ Error Rate & 0.001439 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weighted average multiclass classification results.  
this indicates a perfect balance between avoiding false positives and detecting all true positives. This is an essential feature for security models, as the costs of false negatives can be significant.  
The binary classification results, as shown in Table 5, demonstrate the model's exceptional performance. The model achieved 100% accuracy, specificity, precision, recall, and F1-Score, an excellent detection capability and 0 error rate. This consistency across all metrics indicates that the model is proficient in identifying the correct class labels and maintains this accuracy uniformly throughout the proposed dataset.  
Figure 4 displays the binary classification confusion matrix, indicating a highly accurate predictive model for both 'Attack' and 'No Attack' scenarios. The dataset is composed of 11,121 instances, 2349 were classified as 'No Attack', and 8772 were classified as 'Attack'. The model performed correctly, with zero false positives or false negatives. The absence of off-diagonal values in the matrix confirms this. The model accurately predicted all 'Attack' instances (true positive rate) and all 'No Attack' instances (true negative rate). The scenario presented here demonstrates a binary classification framework with unparalleled perfection in cybersecurity for IoHT. It achieved a 100% success rate in detecting true threats and ensuring non-threat conditions, setting a theoretical benchmark for cybersecurity applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Specificity (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score** \\ \hline No Attack & 100 & 100 & 100 & 100 & 100 \\ Attack & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Binary classification results for each class.  
Figure 4: Analysis of the confusion matrix binary classification results.