## 2 Related Work
### Class Imbalances  
When a class is underrepresented in a dataset, it leads to an imbalance that poses challenges in detecting the minority class, ultimately reducing the performance of intrusion detection systems [58]. In ref. [21], the IoT23 dataset is introduced, highlighting significant class imbalances due to the disparity between malicious and benign behaviors of IoT-connected devices. This work addresses the class imbalance challenge by applying preprocessing techniques like the SMOTE method to enhance the detection of minority class instances in the proposed IDSs. In ref. [18], the ToN-IoT dataset, derived from a large-scale heterogeneous IoT network, was utilized to address class imbalance in both binary and multi-class classification tasks. The study applied SMOTE for class balancing, demonstrating that XGBoost outperformed other ML methods. In ref. [30], ADASYN was employed to address data imbalance by expanding minority class samples, creating a more balanced NSL-KDD dataset. The study focused on mitigating class imbalance to improve model performance and utilized a modified stacked auto encoder for dimensionality reduction to enhance information fusion. In ref. [55], to address the class imbalance issue in the CSE-CIC-IDS2018 dataset, the SMOTE algorithm was applied, along with an improved loss function. These techniques helped balance the dataset, ensuring better model performance, with a LSTM network enhanced by an AM for improved detection. In ref. [57], to tackle the class imbalance issue, a CGAN was utilized to augment minority attack data for multi-class intrusion detection. This approach helped balance the dataset and improve model performance by addressing the underrepresentation of certain attack classes. In ref. [59], a data resampling technique combining the ADASYN and Tomek Links algorithms was proposed to address the class imbalance problem. This approach was applied alongside various deep learning models to improve performance on the NSL-KDD dataset. In ref. [60], a resampling method based on self-paced ensemble and auxiliary classifier generative adversarial networks (SPE-ACGAN) was proposed to address class imbalance. This method mitigated the imbalance by oversampling minority class samples using ACGAN and undersampling majority class samples through SPE. In ref. [61], class imbalance was addressed by using ADASYN to oversample minority-class samples. This technique enhanced the model's ability to detect underrepresented classes, ensuring better performance in classification tasks. In ref. [62], a hybrid approach combining SMOTE and Tomek Links was proposed to address class imbalance in the CICDDoS2019 and Edge-IIoT datasets, effectively mitigating the unbalanced distribution of classes. In ref. [63], the Jaya optimization technique was combined with the SMOTE-ENN method to address class imbalance in the UNSW-NB15 and NSL-KDD datasets, providing an effective solution for improving intrusion detection. In ref. [64], SMOTE and NearMiss-1 were utilized to address class imbalance. The model's performance in multi-class classification was evaluated on the UNSW-NB15 dataset and further validated for robustness using the NSL-KDD dataset. In ref. [65], the imbalance in training data leading to low detection rates of minority attacks was addressed by enriching minority samples using an improved generative adversarial network (IGAN). In ref. [66], a novel model-based generative adversarial network called TDCGAN was proposed to tackle class imbalance in datasets. This approach focused on improving the detection rate of minority class instances while ensuring efficiency in handling imbalanced data. In ref. [67], SMOTE was employed to address class imbalance by oversampling the minority class, thereby reducing the impact of data imbalance on classification performance. In ref.