## 2 Background and Related Work  
Artificial intelligence (AI) is a discipline of computer science that simulates the human brain to identify the best feasible solution to achieve a given goal. ML is a branch of AI that uses the results of previous experiences as future instructions without being explicitly programmed. Supervised, unsupervised, and semi-supervised learning are the three main subcategories of machine learning. DL is a sub-branch of machine learning. In the last decade, much work has been done to combine these strategies to improve cybersecurity. In most application contexts, the effects of DL models are superior to those of classic machine learning (or shallow-model) methods [10; 11]. The following aspects are the most noticeable differences between deep and shallow models: running time, number of parameters, feature representation, and learning capacity. DL models consist of a variety of deep networks. Autoencoders, GANs, and RBMs have supervised learning models. Unsupervised learning models include DBNs, DNNs, CNNs, and RNNs. DL models directly learn feature representations from the source data, such as images and text, without requiring explicit feature engineering. DL approaches provide a major advantage over shallow models for huge datasets. Network architecture, hyperparameter selection, and optimization approach are the major focuses of deep learning research. The ToN-IoT, Edge-IIoT, and UNSW2015 datasets are three current datasets in cybersecurity and the Internet of things that are discussed in this paper. Cybersecurity goals include data protection, resource protection, data privacy, and data integrity. Online, there are several risks and attacks. Fraud detection, virus detection, intrusion detection, spam classification, phishing, and firewall disabling are all common cybersecurity issues [12; 13]. Several solutions have been introduced to tackle cyber-attacks in recent years, but traditional methods fail to solve such attacks based on network traffic. However, the predictive power of deep learning models--especially CNNs--is much better than that of traditional ML models. Recently, researchers have used the DenseNet network and Inception Time. Because of their outstanding performance, they have attracted much interest. The authors of [14] presented a new privacy-preservation-based intrusion detection framework in network traffic Internet of things fog using the SAE technique. The effectiveness of an intrusion detection system based on the ANN technique was then assessed for identifying attacks and typical vectors of the ToN-IoT dataset. [PERSON] et al., in [15], suggested a trustworthy privacy-preserving secured framework for intelligent cities. The privacy module transforms data into a new reduced shape for thwarting inference and poisoning assaults using a Blockchain-based enhanced proof-of-work (ePoW) technique and principal component analysis (PCA). The intrusion detection module implements an enhanced gradient tree boosting method (XGBoost). The authors of [16] used intrusion-detection systems using deep learning models based on ANN, DNN, and RNN by combining the entire UNSW-NB1 dataset into a single CSV file so that DL models could be tested once rather than separately for each file. The dataset was labelled with attack families as new labels. The authors of [17] used IGRF-RFE and applied the hybrid feature selection method to the UNSW-NB15 dataset IDS for MLP-based intrusion-detection systems. IGRF ensemble feature selection and recursive feature removal using MLP are the two feature-reduction phases in IGRF-RFE. Then, as a wrapper feature selection approach, recursive feature elimination (RFE) was used to progressively delete duplicate features on the decreased feature subsets. The authors of [18] suggested IDS--a new distributed ensemble design based on fog computing, combining XGBoost, k-nearest neighbours, and Gaussian naive Bayes as individual learners. Random forests then use the obtained prediction results for the final classification.