## 2 Related Work
### Binary Classification
[21] proposed a two-stage deep learning structure incorporating a gated recurrent unit (GRU) network in the first stage and a denoising auto-encoder (DAE) in the second stage, which achieved an accuracy of 90.21% on Test+ for intrusion detection. Furthermore, ref. [22] tackled class imbalance by integrating CNN-bidirectional long short-term memory (BiLSTM) with ADASYN, reaching an accuracy of 90.73% on Test+. In ref. [23], the authors proposed a hybrid Transformer-CNN deep learning model to overcome these challenges. The model utilized data resampling methods like ADASYN, SMOTE, ENN, and class weights to address class imbalance, achieving an accuracy of 99.71%. In ref. [24], the authors proposed advancements for IDSs in cloud environments by developing and evaluating two innovative deep neural network models. The first model utilized a multi-layer perceptron (MLP) trained with backpropagation (BP), while the second combined particle swarm optimization (PSO) with MLP training. Both models achieved impressive accuracy of 98.97%, demonstrating significant improvements in IDS performance and efficiency for detecting and mitigating intrusions. In ref. [25], the authors demonstrated a notable reduction in the time required for traffic analysis and significant success with their proposed model. Tested on the CSE-CIC-IDS2018 dataset, the model's effectiveness was confirmed. Experimental results revealed that using the ExtraTree algorithm, the model achieved an impressive accuracy of 98.5% for binary classification. In ref. [26], the authors utilized PySpark with Apache Spark in the Google Colaboratory (Colab) environment, relying on the Keras and Scikit-Learn libraries. The training and testing datasets included 'CICIoT2023' and 'TON_IoT'. To enhance the feature set, the datasets were refined using the correlation method. The authors developed a hybrid deep learning algorithm combining one-dimensional CNN and LSTM, obtaining an accuracy of 98.75% for optimal performance. In ref. [27], the authors introduced a new classifier algorithm designed to detect malicious traffic in IoT environments using machine learning techniques. The approach utilized a real IoT dataset that simulates actual traffic conditions, assessing the performance of different classification algorithms to evaluate their effectiveness, achieving an accuracy of 99.2%. In ref. [28], the authors described three essential machine learning techniques used for binary classification. These methods were applied within an IDS designed to detect IoT-based attacks and classify them into two categories, benign and malicious. The study utilized the IoT-23 dataset, a recent and extensive dataset, to create an intelligent IDS capable of identifying and categorizing attack patterns in IoT environments for binary classification, achieving an accuracy of 99%. In ref. [29], the authors examined the factors influencing existing near-Earth remote sensing systems and introduced a spatio-temporal graph attention network (N-STGAT) incorporating node states for application in network intrusion detection in near-Earth remote sensing systems, obtaining an accuracy of 97.88%. In ref. [30], the authors introduced a self-supervised graph neural network for network intrusion detection systems, designed to effectively and thoroughly differentiate between normal and malicious network flow associated with various attack types. To the best of our knowledge, this represents the first graph neural network (GNN)-based method for classification tasks in NIDS using an unsupervised approach, resulting in an accuracy of 99.64%. The Transformer-DNN and Autoencoder-CNN models outperformed existing methods, achieving 99.92% and 99.90% accuracy, respectively, in binary classification using CICIDS2017. With the NF-BoT-IoT-v2 dataset, both models reach 99.98% accuracy. These results represent significant advancements, with a comparison in Table 1.