# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
## 7 Conclusions  
In this research, we proposed a new strategy for IDS development called HRC. This strategy comprises two parts: the regression part to predict the near future behavior and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Traffic** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline Normal & 1.00 & 1.00 & 1.00 & 11,520 \\ \hline Back & 0.99 & 1.00 & 0.99 & 173 \\ \hline IP Sweep & 0.76 & 0.86 & 0.81 & 620 \\ \hline Neptune & 0.95 & 1.00 & 0.97 & 7063 \\ \hline Other & 0.98 & 0.65 & 0.78 & 1097 \\ \hline Port sweep & 1.00 & 0.78 & 0.87 & 500 \\ \hline Smurf & 0.98 & 1.00 & 0.99 & 447 \\ \hline Teardrop & 1.00 & 1.00 & 1.00 & 160 \\ \hline
**Accuracy** & – & – & 0.97 & 21,580 \\ \hline
**Macro average** & 0.96 & 0.91 & 0.93 & 21,580 \\ \hline
**Weighted** & 0.97 & 0.97 & 0.97 & 21,580 \\
**average** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification report of the experiment. Figure 26: Confusion matrix of the experiment on IDS when applying the proposed strategy. decide if an attack will potentially occur, and the classification part to classify the types of those potential attacks correctly so that the IDS will proactively have a solution to deal with them. We suggested using the Conv1D--LSTM model for our regression part, due to its powerful ability to exploit the time-space relationship among the IP packages in the traffic and predict incoming network behaviors, and using a simple CNN model for the primary task of classifying the attacks due to its light structure and excellent classification abilities. Typically, most of the former research only focused on developing the classification algorithm to raise the classification results in IDS frameworks at the current timestamp, with little to no consideration for handling imbalanced data. Our strategy is able to deal with imbalanced data without modifying the data and retains the number of samples as well as the ratio between them; in addition, it also helps the IDS accurately predict the incoming behavior of the network in the near future. In our research, we primarily use the AWID3 dataset, one of the latest and most trustful datasets in the IDS field of research, with an additional NSL-KDD dataset to strengthen the evaluation of the experiments on the AWID3 dataset. The results indicate that the model achieves a high accuracy of 91% overall accuracy in predicting the current and future behavior of IP traffic, specifically 20 IP packages ahead according to our setup. Not only that, but it also achieves a balance in prediction between categories despite the heavy imbalance in sample numbers among categories, particularly F1 scores of 0.83 and 0.82 for the Evil Twins and Bot Net attacks in the AWID3 dataset, with their number of samples only roughly 1/20 compared to the normal category; and 1.00 and 0.99 for Back and Teardrop attacks in the NSL-KDD dataset despite their number of samples being barely 1/50 compared to their normal category. Based on these results, this approach can potentially significantly reduce the false alarming rate and false attacks in IDS classification. One limitation of our paper is that we only tested on available datasets and not in a practical context, which we will aim to achieve in the future. In future research, we will try to deploy our proposed HRC strategy in practical IDS and investigate developing this into an even better framework for IDS that examines lighter models but still raises the prediction accuracy to a greater extent through reinforcement learning algorithms. Our secondary aim is to create datasets that can emphasize a large area of wireless communication from wireless networks to cellular networks (such as 5G) so that these datasets can be used to test the currently proposed approaches for IDS and, furthermore, contribute great material for future across different fields. Conceptualization, K.D.N.D., M.V. and P.F. ; methodology, K.D.N.D. ; software K.D.N.D. ; validation K.D.N.D., M.V. and P.F. ; formal analysis K.D.N.D. ; investigation, K.D.N.D.