## 2 Related Work  
Deep learning (DL) helps researchers and cyber analysts gain insights into threats and stay ahead of opponents. Attack detection continues to be an active area of research that receives considerable attention due to the wide attack vector and the possible impact of attacks on IoT systems. Many research efforts have been carried out to detect network attacks using DLs. Using network traffic analysis, an SDN-based intrusion detection system that is executed as an application module on the controller was developed in [3]. In [4], The Bot-IoT dataset was used to train DRNN and SMOTE-DRNN models, and the simulation results show that high-class imbalance in the training data has a negative impact on the DRNN model's precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM), and Matthew's correlation coefficient (MCC). In [5], the authors had recommended adopting Blockchain to protect FL algorithms in Internet of Things (IoT) devices from attacks. Integrating Blockchain and FL ensures the integrity of trained models, preventing model poisoning attacks. In [6], the authors had suggested a clustering method based on unsupervised feature selection and cluster center initialization for intrusion detection. This method computes initial cluster centers using sets of semi-identical examples that signal dense data space and avoids outliers. A model was created in [7] that extracts important features from the given features and then classifies incursions using a deep learning approach. Notably, underlying data points cannot be considered samples from a single distribution, but rather from two distinct distributions--one generic to all network intrusions and the other domain-specific. A deep learning-based deep belief network (DBN) algorithm was advocated in [8] for intrusion detection systems. The survey presented in [9] offers a thorough analysis of LEACH descendant clustering procedures. This is the first study to categorize LEACH-based routing protocols into CH selection, data transmission, and combined CH selection and data transmission methods.  
The authors in [10] suggested a cyberattack detection framework based on ensemble learning and fog-cloud architecture for IoMT networks. The ensemble design incorporates the individual learners: decision tree, naive Bayes, and random forest. Training the models with just examples of normal classes, a new method is presented in [13] based on long short-term memory (LSTM) autoencoder and one-class support vector machine (OC-SVM) to detect anomaly-based assaults in an unbalanced dataset. Using a variety of machine learning algorithms and ensemble learning, [14] compares several of these intrusion detection systems. Using a random forest regressor technique, a subset of attributes (significant) is selected from the primitive set of attributes, and this subset is then utilized to train different classifiers. Table 1 shows the most significant existing IDS systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Technique** & **Dataset** & **Accuracy** \\ \hline \multirow{3}{*}{[3]} & An intrusion detection system based on the software defined & \multirow{3}{*}{NSL-KDD} \\  & networks model is presented which is executed as an application & \\  & module in the controller & \\ \hline \multirow{2}{*}{[4]} & Develop DRNN and SMOTE-DRNN models & Bot-IoT dataset & 99.87\% \\ \hline \multirow{3}{*}{[6]} & A clustering method based on unsupervised component selection & \multirow{3}{*}{KDD CICIDS2017} & 93.07\% \\  & and initialization of the cluster center & & 88\% \\ \hline \multirow{3}{*}{[7]} & CNN technique with L2 regularization and the dropout methods to & \multirow{3}{*}{InSDN} & 94.06\% \\  & address the overfitting problem & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing IDS approaches.