## 5 Vulnerabilities of SDN to LDDoS Attacks
### Deep Learning-Based LDDoS Detection  
CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Term Memory), and other deep learning models have been applied to increase SDN protection against LDDoS threats [82; 84]. Feature extraction is automatic in deep learning \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Model Type**} & \multirow{2}{*}{**To Increase the Detection Rate by**} & \multicolumn{2}{c}{**Objectives**} & \multicolumn{1}{c}{\multirow{2}{*}{**To Improve Detection**}} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Shottoming/Disadvantages**} \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{[81]} & A multi-feature method based & \multirow{3}{*}{✓} & & & 95.8\% & Not provide a mechanism based on \\  & on the PM algorithm & & & & & & synchronization between SDN \\ \hline \multirow{3}{*}{[85]} & Learning-based approach using & & & & & & \\  & the QMIND framework & & & & & & \\ \hline \multirow{3}{*}{[7]} & Modular architecture based & & & & 98\% & A complex training process and \\  & on SDN & & & & & & high cost of implementation. \\ \hline \multirow{3}{*}{[7]} & Learning-based detection using & & & & 95\% & Increase the controller overhead \\  & stativel and landscapes features & & & & & & \\  & Traffic summation framework & & & & & & and decrease response efficiency. \\ \hline \multirow{3}{*}{[7]} & based on SDN & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{[7]} & A structured convolution feature & & & & & & \\  & optimization method & & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{[80]} & Performance and Features & & & & & 96\% & A legitimate user experiences more delays \\  & framework & & & & & when the network is attacked. \\ \hline \multirow{3}{*}{[80]} & Flow-based detection & & & & & & \\  & framework using ML classifiers & & & & & \\ \hline \multirow{3}{*}{[82]} & CNN-LSTM Model for & & & & & & \\  & SDN-based networks & & & & & limited to traffic types. \\ \hline \multirow{3}{*}{[84]} & CNN-GRU Model for & & & & & & Resource intensive from a training perspective and not designed to detect \\  & SDN-based networks & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis methods used by existing work and their shortcomings or disadvantages. models [115]. The general structure framework for deep learning-based LDDoS attack detection mechanisms is similar to the machine learning classification-based framework, as shown in Figure 6. However, in the case of a deep learning-based model, selecting features is performed automatically. Table 2 summarizes deep-learning-based LDDoS detection mechanisms along with classification-based mechanisms. In [82], [PERSON] et al. present an ensemble convolutional neural network solution for LDDoS attack based on deep learning. To detect LDDoS attack in SDN-based networks, the authors suggest CNN-LSTM, a hybrid Convolutional Neural Network-Long-Short Term Memory model. Custom datasets are utilized to evaluate the strategy's effectiveness. The results are impressive; all of the studied performance metrics have a value greater than 99%. The hybrid model CNN-LSTM outperforms traditional deep learning approaches comparable to MLP (MultiLayer Perceptron) [116] or 1-class SVM [117]. The detector unit in this deep learning-based model evaluates the extractor unit's output and determines whether the entries are classed as harmful or harmless traffic. As a result, it functions as a binary classifier, classifying malicious traffic as \"1\" and non-malicious traffic as \"0\". The labels are connected with the original IP address since a single host might be authentic or fraudulent. In depicting the hybrid CNNLSTM model used in the detector module, the pointers \(\times 1\), \(\times 2\).., \(\times n\) relate to the features associated with each entry. In [84], [PERSON] et al. developed a technique for detecting LDoS attacks based on the hybrid CNN-GRU model: Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU).