## 7 Conclusions  
A new hybrid model for the IoT environment is developed using deep learning models such as GRU and Bi-LSTM, where features such as \"higher-order statistical features (kurtosis, variance, moments), mutual information, symmetric uncertainty, information gain ratio, and relief-based features\" were derived. Further, GRU and BI-LSTM were deployed, where weights of BI-LSTM were optimally chosen via SU-CMO for detecting the attacks. The graph shows that the proposed hybrid classifier + SU-CMO achieves greater accuracy, i.e., \(\sim\)0.84 for dataset 1 and dataset 2, which is higher than the accuracy of other classic and state-of-the-art models such as hybrid classifier + WOA, hybrid classifier + GWO, hybrid classifier + SLnO, NN, SVM, and DT. The SVM model has accomplished worse outputs than other schemes such as HC + ALO, HC + AO, HC + BOA, HC + CMBO, HC + SSA, NN, RNN, and KNN. Although the suggested model shows a higher accuracy level than other models, there are still improvements to be made in terms of accuracy. It is feasible to enhance the accuracy of the model by using the proposed approach in conjunction with several different kinds of deep learning models. In dataset 2, there are several different kinds of attacks; however, in the present paper, all of these types of attacks are grouped under a single category simply referred to as \"attack\". We are considering including the multiclassification of such attacks as part of our future work along with using various kinds of deep learning models.  
Conceptualization, A.S.; methodology, A.S., N.S.G. and P.G.; formal analysis, A.S.; resources, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.S., N.S.G., P.G. and I.P.; supervision, N.S.G., P.G., J.M.C. and I.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Learning Rate** & **HC+ALO** & **HC+AO** & **HC+BOA** & **HC+CMBO** & **HC+SSOA** & **NN** & **RNN** & **Bi-GRU** & **SVM** & **KNN** & **HC+SU-CMO** \\ \hline
60 & 0.66625 & 0.57875 & 0.63375 & 0.5875 & 0.57625 & 0.7075 & 0.62625 & 0.541667 & 0.71125 & 0.75 & _0.8375_ \\ \hline
70 & 0.805 & 0.788333 & 0.583333 & 0.606667 & 0.625 & 0.818333 & 0.588333 & 0.5 & 0.585 & 0.694444 & _0.848333_ \\ \hline
80 & 0.6425 & 0.5725 & 0.655 & 0.6125 & 0.6725 & 0.6875 & 0.6325 & 0.5 & 0.5725 & 0.541667 & 0.7725 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy Analysis of the proposed model over other methods for dataset 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Learning Rate** & **HC+ALO** & **HC+AO** & **HC+BOA** & **HC+CMBO** & **HC+SSOA** & **NN** & **RNN** & **Bi-GRU** & **SVM** & **KNN** & **HC+SU-CMO** \\ \hline
60 & 0.61625 & 0.67875 & 0.72125 & 0.66625 & 0.65 & 0.78625 & 0.625 & 0.6375 & 0.50625 & 0.8075 & _0.8365_ \\ \hline
70 & 0.675 & 0.785 & 0.763333 & 0.595 & 0.658333 & 0.766667 & 0.638333 & 0.68 & 0.591667 & 0.793333 & _0.847333_ \\ \hline
80 & 0.805 & 0.5675 & 0.7275 & 0.755 & 0.6025 & 0.7875 & 0.685 & 0.6625 & 0.57 & 0.7525 & _0.8175_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy Analysis of the proposed model over other methods for dataset 2.