## 3 Methodology
### Proposed System Architecture
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Proposed Work** & **Classifier** & **Dataset** & **Limitations** \\ \hline
[38] & 2022 & \
\begin{tabular}{c} A security mechanism is devised for \\ anomaly detection \\ \end{tabular} & ReLU PPO2 &
\begin{tabular}{c} Oakridge Lab \\ dataset \\ \end{tabular} & Computation overhead increases \\ \hline
[39] & 2022 & \begin{tabular}{c} An analytical model is presented to \\ filter organic traffic flows \\ \end{tabular} & CNN & \
\begin{tabular}{c} CICDS2017, \\ UNSW-NB15 \\ \end{tabular} &
\begin{tabular}{c} Not suitable for resource- \\ constrained environments \\ \end{tabular} \\ \hline
[40] & 2022 & \begin{tabular}{c} A security framework is proposed \\ for DOS detection \\ \end{tabular} & LSTMGRU, \
\begin{tabular}{c} BLS \\ \end{tabular} & N-BaIoT &
\begin{tabular}{c} Communicational delays \\ experienced \\ \end{tabular} \\ \hline
[41] & 2022 & \begin{tabular}{c} An efficient IDS is designed for \\ industrial IoT \\ \end{tabular} & LSTM & \
\begin{tabular}{c} UNSW-NB15, \\ NSL-KDD \\ \end{tabular} &
\begin{tabular}{c} Appropriate for small-scale \\ networks only \\ \end{tabular} \\ \hline
[42] & 2022 & \
\begin{tabular}{c} A malicious entity identification \\ scheme is designed. \\ \end{tabular} & CNN & CICIDS2017 &
\begin{tabular}{c} Demands significant system \\ resources \\ \end{tabular} \\ \hline
[43] & 2021 & \begin{tabular}{c} An extensive intrusion detection \\ mechanism is constituted \\ \end{tabular} & DNN & \
\begin{tabular}{c} NSL-KDD, \\ UNSW-NB15 \\ \end{tabular} &
\begin{tabular}{c} Extensive latencies have been \\ noticed \\ \end{tabular} \\ \hline
[44] & 2021 & \begin{tabular}{c} A multilayer threat classification \\ model is proposed GRUCNN \\ \end{tabular} & ADFA-LD, \
\begin{tabular}{c} KDD99 \\ \end{tabular} &
\begin{tabular}{c} Complexities increases in \\ large-scale industrial networks \\ \end{tabular} \\ \hline
[45] & 2021 & \
\begin{tabular}{c} Suspicious events detection scheme \\ is designed \\ \end{tabular} & LSTM, SFLN & IoT-ID20 &
\begin{tabular}{c} The communication stream is not \\ stable \\ \end{tabular} \\ \hline
[46] & 2021 & \
\begin{tabular}{c} A systematic approach is presented \\ for abnormal traffic detection \\ \end{tabular} & PCA-DL & N-BaIoT &
\begin{tabular}{c} Extensive computational \\ resources required \\ \end{tabular} \\ \hline
[47] & 2021 & \
\begin{tabular}{c} A dynamic traffic analysis scheme is \\ formulated \\ \end{tabular} &
\begin{tabular}{c} Binary, \\ Multiclass \\ \end{tabular} & BOT-IoT & Highly resource consumptive \\ \hline
[48] & 2020 & \
\begin{tabular}{c} An IDS is proposed for large-scale \\ generic networks \\ \end{tabular} & DNN & Mirari dataset &
\begin{tabular}{c} Not efficient for medium-scale \\ networks \\ \end{tabular} \\ \hline
[49] & 2020 & \begin{tabular}{c} A secure communication \\ framework is designed \\ \end{tabular} & Forest PS & \
\begin{tabular}{c} BOT-IoT, \\ CICIDS2017 \\ \end{tabular} &
\begin{tabular}{c} Computational overhead \\ increases \\ \end{tabular} \\ \hline
[50] & 2020 & \begin{tabular}{c} The threat identification and \\ classification model is designed \\ \end{tabular} & \begin{tabular}{c} DT, MLP, \\ LSTM \\ \end{tabular} & \
\begin{tabular}{c} Customized \\ dataset \\ \end{tabular} &
\begin{tabular}{c} Considerable increase in \\ communication delays \\ \end{tabular} \\ \hline
[51] & 2020 & \begin{tabular}{c} An extensive attacks analysis \\ approach is proposed CART \\ \end{tabular} & CNN & \
\begin{tabular}{c} NSL-KDD, \\ KDD-99 \\ \end{tabular} &
\begin{tabular}{c} Significantly complex for \\ large-scale networks \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing literature. softmax is integrated at the output layer. The scheme is occupied with Adam optimizer to acquire the desired performance objectives. For a classified analysis of the system's performance, the designed Cu-DNNGRU is tested, with Cu-LSTMDNN having two layers of neurons, BLSTM, and GRU with four layers of neurons for each classifier.