# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
## 5 Applying the HRC Strategy to an IDS
### Comparison and Validation  
To evaluate the performance of our chosen models in this framework's strategy, we created other hybrid models with combinations of different types of ML and DL algorithms and compared them to the proposed model for accuracy and F1 score in each prediction category and parameter. We used Conv1D-LSTM, regular LSTM and regular CNN1D for our regression part, combining with each of LR, SVC, DNN, LSTM and GRU for the classification part. We combined them and performed the experiment to see which one was the best for our proposed hybrid strategy. Table 3 presents the results of each model's performance. As shown in Table 3, the proposed hybrid model between Conv1D-LSTM and CNN1D has the highest accuracy in prediction and the best set of F1 scores due to having a recursive model and feed-forward model that can capture both space and time relationships between features in the dataset. Regarding those hybrid models that comprise the CNN1D as their detector, they were less efficient than the hybrid model with the recursive detector because they contained only feed-forward neural networks that can only capture the space relationship between features in the dataset. The hybrid models that have an ordinary LSTM model as their detectors, such as LSTM-DNN or LSTM-SVC, are not as good as the Conv1D-LSTM and CNN1D model due to lacking the strength of exploiting the space relationship between category features in the dataset. However, despite having lower accuracy in BN attack prediction, they still reach approximately 91% and 81% overall accuracy when having fewer parameters compared to our proposed model. This accuracy is not too bad. Therefore, in the case of the systems that focus on memory saving more than accuracy, they can be used instead. This trade-off is mentioned by [PERSON] and colleagues in the paper [70], in that the choice between complex or simple approaches for an IDS is not always about which one is better than the other; it is about finding the right balance between accuracy, efficiency, and scalability for each situation. Therefore, the task will determine if the used approach is costly or not in terms of computational cost and if it is easy or hard to scale according to the growing demands. Based on the problem in this paper, which is about classifying the attacks with complex patterns and imbalanced datasets, not only at the current time but also in the near moment in the future, the problem can be considered a complex task, and our proposed strategy with 264,144 parameters is able to handle the task well and can be considered as still relatively small and efficient. Additionally, our strategy can deal with other datasets with more attacks and imbalanced categories, which is mentioned in Section 6. This shows its ability to scale with the growing problems at a certain rate before it cannot handle the problem anymore. And when this happens, we just need to change the models within the strategy to be more suitable for the task. Therefore, compared to other mentioned approaches, the proposed HRC strategy with this setting-up model is one of the brightest candidates for efficient computational cost and scalability. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Hybrid Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{5}{c}{**F1 Score**} & \multirow{2}{*}{**Params**} \\ \cline{2-2} \cline{6-7}
**Detector** & & & & **Normal** & & **WSP** & **ETW** & **BN** & **MW** \\ \hline \multirow{8}{*}{Conv1D–LSTM} & * CNN1D & 0.91 & 0.94 & 0.85 & 0.83 & 0.82 & 0.86 & 264,144 \\ \cline{2-7}  & DNN & 0.91 & 0.94 & 0.84 & 0.91 & 0.41 & 0.86 & 272,896 \\ \cline{2-7}  & GRU & 0.91 & 0.94 & 0.84 & 0.91 & 0.40 & 0.86 & 327,200 \\ \cline{2-7}  & LSTM & 0.91 & 0.94 & 0.84 & 0.91 & 0.40 & 0.86 & 344,608 \\ \cline{2-7}  & LR & 0.81 & 0.94 & 0.88 & 0.00 & 0.43 & 0.62 & 262,987 \\ \cline{2-7}  & SVC & 0.91 & 0.94 & 0.83 & 0.84 & 0.72 & 0.86 & 262,987 \\ \cline{2-7}  & XGBoost & 0.72 & 0.91 & 0.10 & 0.27 & 0.05 & 0.00 & 263,011 \\ \hline \hline \end{tabular}
\end{table}