## 4 Discussion  
The results of our experimental evaluation of the hybrid IoHT attack detection model demonstrate a highly effective defense mechanism against cyber threats in IoHT. By integrating ANN with BLSTM and GRU architectures, our model achieved impressive results in both multiclass and binary classification scenarios, as evidenced by near-perfect accuracy, precision, recall, and F1-Score scores. Our proposed model performed better than existing IDSs in the IoHT and IoT domains. While methodologies like LSTM with CNNs and various feature extraction methods combined with machine learning models offer high accuracy, they often need to provide complete coverage against many attacks, as shown in Table 6. For instance, the model in [10] has a mean accuracy of 93.74%, which, is notably lower than the 100% accuracy achieved by our model in a binary classification scenarios. Similarly, the hybrid approach employing CNN-BLSTM-GRU architectures, as reported in reference [16], achieves an accuracy of 99.25%, which is still lower than our model's multiclass accuracy of 99.86%. The proposed model excels in its robustness and dynamic adaptability. The ANN component is critical in processing complex patterns in IoHT data, effectively filtering and identifying potential threats. Additionally, the BLSTM layer enhances the model's ability to understand bidirectional dependencies within the data, which is particularly useful for capturing evolving patterns. Moreover, including GRU layers helps manage long-term dependencies, enabling the model to maintain high performance even with extended data sequences typical in IoHT environments. The model's high precision and recall metrics across all classifications demonstrate its accuracy and reliability, minimizing the risk of false positives and negatives. Such reliability is crucial in healthcare settings where patient data integrity and confidentiality are paramount. By accurately detecting and classifying cyber-attacks, the model supports the continuous availability and reliable performance of healthcare services, safeguarding sensitive health data against unauthorized access and potential tampering. As shown in Figure 5, the proposed model obtained the best recalls, which confirms that it better detects abnormal instances of actual attacks compared to recent works. \begin{table}
\begin{tabular}{c c c} \hline
**Ref.** & **Dataset Used** & **Methodology** & **Results** \\ \hline
[9] & UNSW NB15 & Hybrid approach (LSTM + CNN) & 98\% accuracy \\
[10] & 25,000 instances & Four-layer deep Fully Connected network & Accuracy: 93.74% precision: 93.71\% \\
[11] & SDNOT & LSTM for network assaults in IoT networks & Accuracy: 0.971 \\
[12] & IEEE Dataport & Various feature extraction methods with ML models & VGG-16 with stacking: 98.3% accuracy \\
[13] & NID Dataset, BoT-IoT & CNN for anomaly-based IDS & Accuracy: 99.51% (D19), 92.85% (BoT-IoT) \\
[16] & IoT-based botnet & Hybrid deep learning (CNN-BLSTM-GRU) for IoT & Accuracy: 98.34% (IoT-based botnet), 92.25\% \\
[17] & NSL-KDD & Deep learning for intrusion detection in social IoT & (CNN-BLSTM-GRU) \\
[26] & ECU-IoHT & Deep neural network for cyber-attack detection ANN & Accuracy: 99.85%, Nmap port scan and DDOS recall: 92%, \\
[27] & wusst+ehms-2020, ECU-IoHT & DNN-FL for anomaly detection in IoHT data & Accuracy: 91.40% (wusst+ehms-2020), 98.47% (ECU-IoHT) \\ Proposed & ECU-IoHT & Hybrid IoHT detection model & Multiclass accuracy: 99.86, binary class accuracy: 100. \\  & & ARP spoofing, Nmap port scan, and DDOS recall: 100\%. \\ \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of various existing IDSs based on the IoT and IoHT networks. \begin{table}
\begin{tabular}{c c} \hline
**Metric** & **Value** \\ \hline Accuracy & 100 \\ Specificity & 100 \\ Precision & 100 \\ Recall & 100 \\ F1-Score & 100 \\ Error Rate & 0.0 \\ \hline \end{tabular}
\end{table} 
Table 5: Weighted average binary classification results.