## 2 Related Work  
Researchers widely explore artificial intelligence (AI) methods such as ML and DL to propose efficient NIDS solutions over the last decade. It was observed from the recent trends on NIDS that DL algorithms are preferred over ML during the past three yearsdue to the advancement in Graphical Processing Units (GPUs) technology, which have solved the fast computation requirement of the DL algorithms [50]. This has motivated the researchers to use the DL algorithms to propose efficient security solutions in an IoT network that process vast amounts of raw data. The DL can learn the complex pattern by utilizing its deep structure and help in classifying the benign and anomaly traffic. Researchers in the context of NIDS widely utilize ML algorithms. For instance, [PERSON] et al. proposed an IDS using a fast-learning network with the particle swarm algorithm [51]. Although efficient enough to predict most attacks, their model's performance in detecting the minority class label was not very promising. Similarly, [PERSON] et al. proposed their methodology using the ensemble approach considering multiple extreme learning machines by utilizing the BAT optimization algorithm during the ensemble pruning stage [52]. In another notable work, a multilevel semi-supervised ML model is proposed by [PERSON] et al. by combining the clustering concept with the Random Forest (RF) algorithm [53]. Their methodology performed well in detecting the attack classes due to multiple levels. Researchers also adopt different hybrid approaches to combining ML and DL methods to develop efficient NIDS solutions. The DL methods are explored in all those methodologies for feature reduction and complexity reduction purposes, followed by an ML predictor. For instance, a hybrid approach of combining autoencoder (AE) and the RF is adopted by [PERSON] et al. by only utilizing the encoder part of AE [54]. Their nonsymmetric solution performed well in detecting the anomalies except for few labels due to lower instances. Similarly, another hybrid idea is given by [PERSON] et al. by combining sparse AE with support vector machine (SVM) [55]. This methodology also struggled to detect the minority anomaly labels. Another hybrid approach is coined by [PERSON] et al. by using the ensemble approach utilizing the voting to combine the deep belief network (DBN) with SVM [56]. Researchers have also used standalone DL [57; 58] algorithms such as AE, recurrent neural network (RNN), DBN, convolutional neural network (CNN), Morlet wavelet neural network (MWNN) [59], etc., to propose efficient NIDS models. For instance, an RNN based NIDS is proposed by [PERSON] et al. by utilizing Gated Recurrent Units (GRUs) as the memory unit [60]. Similarly, a CNN-based solution is presented by [PERSON] et al. by using the principal component analysis and AE for feature extraction tasks followed by CNN for prediction [61]. Their proposed methodology only performed well for the class label with a more significant number of instances. Another highly complex NIDS solution is provided by [PERSON] et al. by combining the CNN with the bidirectional Long short-term memory (LSTM) [62]. [PERSON] et al. provided a complex solution based on the combination of different optimization algorithms such as particle swarm, fish swarm, and genetic algorithms with the DBN. The NIDS solutions are also being proposed by many researchers using the DNN approach. For instance, an efficient DNN-based NIDS is proposed by [PERSON] et al., consisting of four hidden layers [63]. The model achieved high-performance results to classify the KDD cup'99 and NSL-KDD datasets. Their proposed solutions did not perform efficiently in detecting User to Root (U2R) attack instances. Another notable work proposed by [PERSON], who proposed and DNN-based IDS for the adversaries, is studying the role of each feature in producing adversarial examples [64]. Similarly, a hybrid scalable DNN framework is proposed by [PERSON] et al.