## 2 Related Work  
In IoT networks, cyberattack detection is typically a classification challenge. These challenges can be successfully resolved through the use of machine learning/deep learning approaches. An overview of various current machine learning/deep learning-based studies for IoT intrusion detection is provided in this section. To detect atypical attacks, [PERSON] et al. [8] developed a flow mining-based method. They used MIT Lincoln Laboratories DDoS data and traces from the Slammer and Code Red worms to validate the effectiveness of their methodology. A deep defense network security architecture was also developed by [PERSON] et al. [9], who suggested using data mining techniques to examine the alarms gathered by the distributed intrusion detection and prevention system (IDS/IPS). The author employed three distinct types of data mining methods to implement the prototype, and they used it in DDoS attack detection to assess the efficacy of the suggested defense architecture. In terms of the attack detection rate and FPR, it performed well. A technique based on the combination of a PCA and an optimized support vector machine (SVM) was presented by [PERSON] and [PERSON] [10]. SVM parameters and kernels were optimized using recommended automatic parameter selection, which reduced the training time and improved accuracy for a few attacks, such as R2L and U2R. In contemporary networks, other well-liked machine learning techniques, including multilayer perceptron (MLP), random forest (RF), and Naive Bayes (NB), have also been employed to identify threats [11; 12; 13]. Unfortunately, shallow learning hinders the effectiveness of these conventional machine learning techniques, making them unable to offer a workable solution for a sizable amount of traffic data. Using several ML algorithms from the Weka Data Mining tool, [PERSON] and [PERSON] [14] examined and assessed the performance of Bagging, AdaBoost, Stacking, Decorate, Voting, and random forest. Compared to single classifiers, the ensembles used in [14] that aggregate the results of their base classifiers using Stacking and Decorate procedures took longer to train and test than classifiers that employed other ensemble techniques. A growing number of deep learning-based models have achieved exceptional performance as deep learning has advanced [15; 16; 17]. An IDS based on the recurrent neural network (RNN) was proposed by [PERSON] et al. [18]. The design approach outperformed conventional classification techniques in terms of accuracy and detection rate for both binary and multiclass classification. To obtain more accurate detection by aggregating traffic characteristics, [PERSON] et al. [19] suggested an intrusion detection model based on long short-term memory (LSTM) and a multimodal deep autoencoder. To mitigate DDoS attacks in cloud computing, [PERSON] et al. [20] employed principal component analysis and linear discriminant analysis in conjunction with a hybrid, nature-inspired metaheuristic algorithm called Ant Lion optimization for feature selection and artificial neural networks to classify and configure the cloud server. An IoT intrusion model leveraging CNN and grey wolf optimization (GWO) was applied to NID [21]. To address the issue of class imbalance in the dataset under consideration, [PERSON] et al. created a two-branch CNN and utilized feature fusion [22]. Their idea was more efficient in terms of execution time and had higher accuracy in detecting a minor class of anomalies. NID was applied to raw packet-level communication by [PERSON] et al. [23]. To extract significant spatial and temporal information, they combined CNNs and LSTMs, which resulted in greater detection rates than when utilizing each of these components separately. The performances of eight distinct machine learning algorithms were examined in [24] against six datasets, including KDD-99, NSL-KDD, UNSW-NB15, Kyoto2006+, and WSN-DS CICIDS2017. The algorithms include DNN, logistic regression (LR), NB, SVM, Adaptive Boosting (AB), KNN, DT, and RF.