## 2 Related Deep Learning Algorithms
### Summary  
Considering the differences between the deep learning methods discussed above, selecting the appropriate model may be difficult. Table 1 shows the comparison of the described DL methods. When data appear sequentially during network issues and hidden channels, the seq-to-seq model may model those dependencies, thus providing better results. However, why not use a \"window\" data analysis or limit its use to single samples? However, keep in mind that deep neural networks use their full potential when they are used for multi-dimensional data; hence, about the speed of operation, the necessity to have a large set of data and potential difficulties in the learning process (e.g., GAN mode collapse) in case of simple, linear applications, classic methods may be a better solution. In different network traffic scenarios, the choice of method depends on the specific requirements. AE and VAE are suitable for scenarios where the modeling of normal behavior and detection of deviations are critical [20]. Applying GANs, seq2 seq, transformers, and similar complex models to anomaly detection should be carefully considered. These methods can sometimes represent an \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Autoencoders (AE)** & \begin{tabular}{c} **Variational** \\ **Autoencoders** \\ **(VAEs)** \\ \end{tabular} & \begin{tabular}{c} **Generative** \\ **Adversarial** \\ **(VAEs)** \\ \end{tabular} & \begin{tabular}{c} **Sequential** \\ **Models** \\ **Networks (GANs)** \\ \end{tabular} & **Transformers** \\ \hline \multirow{10}{*}{\
\begin{tabular}{c} Advantages \\ relevant \\ low-dimensional \\ \end{tabular} } & It efficiently models & Extends AE by & & & Ideal for data \\  & & learning to map & & that are best \\  & & from input data to & & & \\  & new data samples. & a distribution, & Excellent in & represented as & & Global context \\  & & enabling Bayesian & learning a & sequences, & & modeling with \\  & a practical & inference and & generative data & & such as & \\  & compression & more flexible & distribution model, & network packet & & Highly scalable \\  & function to map & anomaly detection. & & set for & generating realistic & Incorporating & and efficient for \\  & input data into a & Valid for broader & samples. & LSTMs and & & high- \\  & relevant & applications, & BiGAN & attention & & dimensional \\  & low-dimensional & including & modifications can & mechanisms & & \\  & representation. & controlled sample & be used to analyze & can model time & Captures \\  & Useful for & generation and & and detect hidden & dependencies & long-range \\  & dimensionality & true probability & contents. & and process & dependencies \\  & reduction, noise & measures for & & sequential data & effectively. \\  & removal, feature & uncertainty & & in parallel. & \\  & extraction, and data & quantification. & & & \\  & compression. & & & & \\ \hline \multirow{10}{*}{
\begin{tabular}{c} Limitations \\ \end{tabular} } & Struggles to & & Classic GANs lack & & \\  & reconstruct data & It is more complex & a direct & & \\  & significantly & to implement and & mechanism for & & \\  & different from the & train due to its & generating & LSTM-based & High \\  & training data, & probabilistic & samples similar to & models may be & computational \\  & limiting its & nature. & known samples, & slow during & cost and data \\  & effectiveness in & It may require & limiting direct & evaluation due & requirements. \\  & diverse or evolving & careful tuning of & applicability for & to their & Sensitive to hyperparameter \\  & network & parameters to & anomaly detection. & sequential & \\  & environments. & & Training can be & nature. & selection. \\  & Prone to overfitting, & achieve optimal & performance. & challenging due to & \\  & especially with large & issues like mode & & \\  & latent dimensions. & & collapse. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of DL methods.