## 2 Related Work  
Some authors simplify their solutions and obtain better model performance results by applying binary-class classification methods while using multi-class datasets. The utilization of a machine learning model that is based on multilayer perceptron (MLP) techniques in order to detect cyber-attacks is proposed in [18]. This is accomplished by utilizing the NF-ToN-IoT dataset, which includes a collection of various cyber-attacks. Through the utilization of a multi-class dataset, the suggested MLP model has been trained and tuned to differentiate between normal and malicious behavior. The effectiveness of the MLP model is demonstrated by the results, achieving an accuracy of more than 90 percent during both the training and validation phases. The TON_IoT multi-class unbalanced network dataset was utilized in [19]. The SMOTETomek approach was employed to address the problem of class imbalance. The model was constructed using the Stochastic Gradient Descent (SGD) optimizer and the binary cross-entropy loss function. It achieved an accuracy of 91% for SVM and 98% for Random Forest. Employing the NF-ToN-IoT multi-class dataset as a benchmark, the suggested model [20] significantly surpasses previous models, underscoring its effectiveness and progress in the domain. The evaluated optimizers comprise Adam, RMSprop, Adagrad, and SGD, with Adam emerging as the most efficacious optimizer for this particular intrusion detection task on the NF-ToN-IoT dataset. We achieved 99.04% accuracy using binary classification. A multi-class data stream from a real-world GSP system was utilized in [21]. This paper proposes a unique framework that integrates CNN and autoencoder-based LSTM methods to enhance anomaly detection in Industrial IoT networks. The authors illustrate that the two-step sliding window method can enhance temporal feature extraction via LSTM, which aggregates the outputs of CNNs within the integrated framework. Anomaly detection was approached as a binary classification problem in the trials undertaken, yielding accuracies of 92.63%, 86.66%, and 89.21% for KNN, RF, and SVM, respectively. The BiLSTM is proposed [22] for anomaly-based intrusion detection systems in IoT networks. The BoT-IoT and ToN-IoT datasets are utilized for the identification of IDS in IoT networks. The gain ratio is utilized for picking optimal features, offering advantages over information gain when it is not biased towards any one feature. The studies conducted by the authors show the findings of binary classification, despite the fact that the datasetsemployed are multi-class. For the BoT-IoT dataset, the BiLSTM obtains an accuracy of 98.76%, whereas for the ToN-IoT dataset, it achieves 96.84% accuracy. Researchers in [23] performed tests on the UNSW-NB15 dataset, which comprises ten classes, including one normal class, three major classes, and six minor classes, within a multi-classification framework. To generate believable synthetic data for minor attack traffic, the authors made use of a state-of-the-art generative model. The authors specifically focused on reconstruction errors, Wasserstein distance-based generative adversarial networks, and autoencoder-driven deep learning models. The accuracy achieved for multi-class classification is 82.0% for Generic (number of records in dataset is 22.81%), 50.2% for Exploits (number of records in dataset is 19.04%), 81.9% for Fuzzers (number of records in dataset is 10.37%), 29.1% for DoS (number of records in dataset is 6.99%), 51.3% for Reconnaissance (number of records in dataset is 5.98%), 77.5% for Analysis (number of records in dataset is 1.14%), 91.5% for Backdoors (number of records in dataset is 0.99%), 58.9% for Shellcode (number of records in dataset is 0.65%), and 56.8% for Worms (number of records in dataset is 0.07%), where the number of Normal records is 31.94%. The authors draw the conclusion that while the suggested framework does enhance classification performance, the issue of relatively low detection rates for certain classes remains.