## 2 Related Work
### Multi-Class Classification
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Author** & **Dataset** & **Year** & \begin{tabular}{c} **Utilized** \\ **Technique** \\ \end{tabular} & **Accuracy** & **Contribution** & **Limitations** \\ \hline [PERSON] & & & & & \begin{tabular}{c} To overcome the limitations of traditional \\ methods, this paper presents a statistical \\ approach for intrusion detection. It includes \\ feature extraction, classification using a \\ multilayer CNN with softmax activation, and \\ additional classification through a \\ multilayer DNN. \\ \end{tabular} & \begin{tabular}{c} \(\bullet\) Restricted to multi-class classification. \\ The proposed IDS exhibits high \\ accuracy and robust performance \\ metrics; however, it has not been \\ evaluated on diverse datasets or in \\ real-world scenarios, which may \\ impact its generalizability. \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON] \\ [PERSON] \\ et al. [51] \\ \end{tabular} & NSL KDD & 2021 & \begin{tabular}{c} Sparse Stacked \\ Auto-Encoders \\ \end{tabular} & 98.5\% & \begin{tabular}{c} This work introduces a controller module for a \\ SDN-based IDS, which includes pre-training \\ with sparse stacking autoencoders, training \\ with a softmax classifier, and \\ parameter optimization. \\ \end{tabular} & \begin{tabular}{c} Restricted to multi-class classification. \\ The proposed IDS demonstrates high \\ accuracy; however, it has not been \\ tested on distributed SDN networks \\ or advanced deep learning techniques \\ such as GANs. Additionally; it could \\ benefit from improved hardware to \\ accelerate the training process. \\ \end{tabular} \\ \hline [PERSON], & & & & \begin{tabular}{c} Restricted to multi-class classification. \\ The LSTM-based intrusion detection method demonstrates high accuracy \\ but may face challenges in \\ generating to datasets \\ beyond NSL-KDD. \\ \end{tabular} \\ Smart & NSL KDD & 2020 & LSTM & 96.9\% & \begin{tabular}{c} Restricted to multi-class classification. \\ The LSTM-based intrusion detection method demonstrates high accuracy \\ but may face challenges in \\ generating to datasets \\ beyond NSL-KDD. \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON] \\ et al. [34] \\ \end{tabular} & \begin{tabular}{c} CSE-CIC- \\ IDS2018 \\ \end{tabular} & 2019 & MLP & 98.31\% & \
\begin{tabular}{c} In this article, the authors assess deep learning \\ algorithms for network intrusion detection by \\ exploring various frameworks, including \\ Keras, TensorFlow, Theano, first.ai, and \\ PyTorch, utilizing the \\ CSE-CIC-IDS2018 dataset. \\ \end{tabular} &
\begin{tabular}{c} The study emphasizes fast.ai’s \\ superior performance and efficiency \\ but has not been tested on additional \\ datasets or in real-world \\ environments. Future research should \\ concentrate on hyperparameter tuning and the investigation of other \\ deep learning algorithms. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table} \begin{tabular}{c c c c c c c c} \hline
**Author** & **Dataset** & **Year** & \
\begin{tabular}{c} **Utilized** \\ **Technique** \\ \end{tabular} & **Accuracy** & **Contribution** & & **Limitations** \\ \hline [PERSON] & & & & & & & & \\ [PERSON] and & CSE-CC- & 2022 & LSTM & 99\% & The increasing demand for cybersecurity & \(\bullet\) & Restricted to multi-class classification. \\ [PERSON]. & IDS2018 & 2022 & LSTM & 99\% & The proposed LSTM-based intrusion & \(\bullet\) & The proposed LSTM-based intrusion \\ [PERSON] & & & & & & & detection system achieves high \\ [PERSON] and & CSE-CC- & IDS2018 & 2022 & LSTM & \(\bullet\) & & & accuracy but faces challenges due to \\ [PERSON]. & IDS2018 & 2020 & DNN & 90\% & The study employs the significance of effective network & \(\bullet\) & Restricted to multi-class classification. \\ \hline \end{tabular} 
\begin{tabular}{c} [PERSON] & CSE-CC- \\ [PERSON] et al. [54] & IDS2018 & 2020 & DNN & 90\% & In this paper, we evaluate our DNN model, which has achieved a significant detection accuracy of around 90\%.