## 2 Related Work
### Multi-Class Classification
[17] reported that using PCA, RF, LDA, and QDA models achieved 99.6% accuracy for multi-class classification. PCA was used for dimensionality reduction, and the reduced features were utilized to construct several classifiers for IDS development. In ref. [23], the authors introduced a hybrid Transformer-CNN deep learning model designed to tackle these challenges. The model incorporates data resampling techniques such as ADASYN, SMOTE, edited ENN, and class weights to mitigate class imbalance, achieving an accuracy of 99.02%. In ref. [31], the authors present a conditional generative adversarial network (CGAN) augmented by bidirectional encoder representation from transformers (BERT), a powerful pre-trained language model, designed to enhance multi-class intrusion detection. The approach uses CGAN to generate additional data for minority attack classes, effectively tackling class imbalance. Moreover, BERT is integrated into the CGAN discriminator, improving feature extraction and strengthening input-output dependencies, thereby boosting detection performance through adversarial training, resulting in an accuracy of 87.40%. In ref. [27], the authors propose a novel classifier algorithm for detecting malicious traffic in IoT environments using machine learning techniques. The method employs a genuine IoT dataset representing real-world traffic patterns and evaluates the effectiveness of various classification algorithms to assess their performance, achieving an accuracy of 99.2%. In ref. [32], an RNN and a DNN were utilized to achieve accuracy rates of 98.68% and 98.95%, respectively. In ref. [33], the authors proposed a multilayer CNN integrated with LSTM, which achieved remarkable accuracy of 99.5%. This approach utilized CNN layers to extract and select features, followed by a softmax classifier to categorize network intrusions. In ref. [34], the authors presented an RF model that achieved an accuracy rate of 98.3%. The study extended its attack detection method to the UNSW-NB15 dataset, reaching 98.3% accuracy in multi-class classification tasks. A hybrid model proposed by ref. [20], combining LSTM, CNN, and SVM, achieved an accuracy of 98.47%. This approach created a HSDL architecture integrating SVM, CNN, and LSTM to analyze semantic information from network traffic. The study also included AES encryption for cloud storage security, optimized using the CMBA technique. In ref. [24], the authors introduced enhancements for IDS in cloud environments by designing and assessing two novel deep neural network models. The first model utilized MLP optimized through BP, while the second combined MLP with PSO. These models significantly enhanced IDS effectiveness and efficiency, achieving accuracy of 98.41%. In ref. [35], the authors underscore the critical importance of cyber security in monitoring and safeguarding network infrastructures against vulnerabilities and intrusions. They emphasize that advancements in machine learning, particularly deep learning, have significantly improved the early detection and prevention of attacks through advanced self-learning and feature extraction techniques. Their research utilized deep learning to analyze the CSE-CIC-IDS2018 dataset, which included both normal network behavior and various attacks. The evaluation of the LSTM model demonstrated a remarkable detection accuracy of 99%. In ref. [29], the authors analyzed the factors affecting existing near-Earth remote sensing systems and proposed an N-STGAT that integrates node states for use in network intrusion detection in near-Earth remote sensing systems, obtaining an accuracy of 93%. In ref. [36], the authors propose a collaborative federated learning approach that facilitates the sharing of cyber threat intelligence (CTI) between organizations, with the goal of developing a more efficient ML-based network intrusion detection system (NIDS). By implementing LSTM on the NF-BoT-IoT-v2 dataset, the model achieved an accuracy of 94.61%.