## 2 Related Work
### Challenges
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Dataset** & **Year** & \begin{tabular}{c} **Utilized** \\ **Technique** \\ \end{tabular} & **Accuracy** & **Contribution** & **Limitations** \\ \hline [PERSON] and [PERSON] [35] & & & & & \begin{tabular}{c} The increasing demand for cyber \\ security highlights the need for \\ robust network monitoring. The study applied deep learning to \\ the CSE-CIC-IDS2018 dataset, achieving 99\% detection \\ for identifying network attacks. \\ \end{tabular} & \begin{tabular}{c} \(\bullet\) Multi-class \\ classification only. \\ The proposed LSTM-based \\ intrusion detection system \\ achieves high accuracy but faces challenges relating to \\ dataset imbalance and \\ large size, which may \\ impact accuracy and \\ complicate model design. \\ \end{tabular} & \begin{tabular}{c} \(\bullet\) Multi-class \\ classification only. \\ The proposed LSTM-based \\ intrusion detection system \\ achieves high accuracy but \\ faces challenges relating to \\ dataset imbalance and \\ large size, which may \\ impact accuracy and \\ complicate model design. \\ \end{tabular} \\ \hline [PERSON] et al. [29] & NF-BoT-IoT-v2 & 2023 & N-STGAT & 93\% & \begin{tabular}{c} Examples the factors influencing \\ existing near-Earth remote \\ sensing systems and introduces \\ an N-STGAT that incorporates \\ node states for application in \\ network intrusion detection in \\ near-Earth remote \\ sensing systems. \\ \end{tabular} & \begin{tabular}{c} A limitation of this study \\ is its dependence on a \\ single dataset for \\ evaluation, which may \\ restrict the N-STGAT \\ model’s ability to \\ generalize to \\ diverse network \\ environments with \\ varying characteristics. \\ \end{tabular} \\ \hline [PERSON] et al. [36] & NF-BoT-IoT-v2 & 2023 & LSTM & 94.61\% & \
\begin{tabular}{c} A collaborative federated \\ learning approach is proposed \\ to enable the sharing of CTI \\ between organizations, aiming \\ to develop a more efficient \\ ML-based NIDs. The \\ implementation of LSTM on the \\ NF-BoT-IoT-v2 dataset achieved \\ an accuracy of 94.61\% \\ \end{tabular} &
\begin{tabular}{c} A limitation of this study \\ is the trade-off between \\ slightly reduced \\ classification performance \\ and maintaining privacy, which may affect the \\ model’s ability to fully \\ match centralized learning \\ methods in terms \\ of accuracy. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ADASYN-SMOTE for binary classification and enhanced SMOTE for multi-class classification, combined with ENN. The Autoencoder enhances the preprocessing of network traffic data by improving feature representation and balancing class distributions, which significantly boosts the CNN's ability to classify and detect rare attack types. Meanwhile, the transformer excels in capturing contextual relationships within data, enabling the analysis of complex patterns and dependencies, while the DNN leverages these insights for precise classification. Both models are optimized for scalability and performance, efficiently handling large-scale datasets while maintaining real-time processing capabilities. Their robustness has been validated through extensive testing on the CICIDS2017 and NF-BoT-IoT-v2 datasets, confirming their effectiveness across diverse network environments and attack scenarios. Designed with real-world deployment in mind, these models minimize false positives and negatives, ensuring their applicability in live network settings. Moreover, a focus on comprehensive evaluation metrics beyond accuracy provides a holistic assessment of performance, addressing potential challenges in detection reliability and practical application.