# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 6 Conclusions  
In recent years, the significance of detecting anomalies and malicious attacks in IoT has surged. As the frequency of such attacks continues to rise, the need for robust tools capable of swiftly and accurately identifying intrusions has become paramount. In this research, we introduce an innovative approach leveraging ensemble-based deep learning models for the rapid and precise detection of intrusions in IoT environments while minimizing false positives and negatives. Our proposed models harness the power of three distinct deep learning models: CNN, GRU, and LSTM, each offering unique classification strengths. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline CNN \(\rightarrow\) LSTM & 0.99734 & 0.997009 & 0.999001 & 0.998004 \\ CNN \(\rightarrow\) GRU & 0.996016 & 0.995025 & 0.999001 & 0.997009 \\ CNN & 0.947867 & 0.970874 & 0.769231 & 0.858369 \\ GRU & 0.909091 & 0.909091 & 0.952381 & 0.930233 \\ LSTM & 0.95498 & 0.985222 & 0.869565 & 0.923788 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result experiment for accuracy, precision, recall, and F1 score. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Techniques Used** & **Accuracy Range** \\ \hline
[44,45] & MLP & 85.2–87.8\% \\
[48] & Dense random neural network & 88.3\% \\
[49] & RNN & 89.5\% \\
[50] & APSO-CNN & 88.7 \\
[52,53,54] & CNN & 90.5 \\
[55] & ANN & 92.5\% \\
[57] & MLP & 87.6\  
These models are thoroughly evaluated using the NSL-KDD open-source dataset, and their performance is benchmarked against standalone models used within the ensemble. Additionally, we compare our results with previous research that employed the NSL-KDD network dataset. Experimental findings unequivocally demonstrate that our proposed model achieves exceptional scores across critical metrics, including accuracy, precision, recall, and F1-Score. In this research, an array of deep learning models encompassing convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are harnessed. An innovative ensemble deep learning framework is also introduced, incorporating a voting mechanism within the model's architecture. This unique approach streamlines the computation and acquisition of hierarchical patterns in the data. Our comprehensive analysis involved comparing the performance of these ensemble deep learning classifiers and traditional deep learning techniques. Notably, the standout models were CNN-LSTM and CNN-GRU, both achieving remarkable accuracy rates of 99.7% and 99.6%, coupled with exceptional F1 scores of 0.998 and 0.997, respectively. Conceptualization, A.O. ; methodology A.O. ; software, A.A.T. ; validation, A.A.T. and A.O. ; formal analysis, A.A.T. and A.O; investigation, A.A.T. and A.O. ; resources, A.O; data curation, A.A.T. ; writing--original draft preparation, A.A.T. and A.O. ; writing--review and editing, A.A.T. and A.O. ; visualization, A.A.T. and A.O  
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