## 3 Related Works  
Recent DDoS detection research utilizing machine learning approaches has achieved promising results. These systems can intelligently understand the underlying data properties without explicitly specifying normal and harmful behaviors, bypassing the limits of conventional detection schemes. The DDoS detection problem is a binary classification problem in which the observed traffic is either normal or attack traffic. Moreover, detection techniques have used deep learning more often in recent years to find DDoS attacks and presented several approaches. Of various recent notable works in this field, some utilized convolutional networks, some utilized recurrent neural networks, especially LSTM and bidirectional LSTM, and some used autoencoder, an unsupervised learning approach, to discover non-linear characterizations from input data, and would then perform a classification algorithm to differentiate malicious traffic from genuine traffic. In 2017, [PERSON], [PERSON], and [PERSON] [12] developed a deep learning algorithm named \"DeepDefense\", a model that uses a deep learning model to detect DDoS attacks. To carry out their research, they used CNN, as well as several distinct variants of RNN (such as LSTM and the gated recurrent unit neural network (GRUNN)), and the random forest (RF) method. The study included a comparison analysis between several deep learning methodologies and between deep learning and machine learning algorithms (it selected RF). DeepDefense put into action four deep learning models: LSTM, CNN-LSTM, GRU, and 3-LSTM. We compared the results of these models with one another. With an accuracy of 98.410% and an area under the curve (AUC) score of 99.450%, the top deep learning model, 3 LSTM, was able to identify DDoS attacks. [PERSON] et al. [13] found that stacking two autoencoders allowed for the learning of more complex feature-based correlations. For intrusion detection, they combined the stacked autoencoder with a random forest classifier. They asserted that the soft-max layer was less effective than traditional classifiers. In 2019, [PERSON] and [PERSON] [14] presented a model-based deep learning method that utilized CNN and LSTM to train the spatial-temporal characteristics of network flows. It used two datasets for training and testing: the ISCX2012 dataset [15] and CICIDS2017 [16]. The results show that the model achieved 0.9669 in precision, 0.9649 in recall, 0.9657 in F1-score, and 0.9666 in accuracy. The model also returned good results when using CI-CIDS2017, where it achieved 0.9797 in precision, 0.9765 in recall, 0.9780 in F1-score, and 0.9772 in accuracy. Some studies developed hybrid deep learning models. [PERSON] et al., 2021 [17] proposed a model that combined two types of deep learning, LSTM and CNN, to detect an attack. Mininet generated the dataset dynamically and utilized OpenFlow switches and Floodlight as an external controller. Based on the findings, RNN LSTM outperformed linear-based models like SVM (86.85%) and Naive Bayes (82.61%), achieving an accuracy of 89.63% compared to their respective scores. Their model had an accuracy of 99.4%, while the KNN technique, based on linear models, had an even higher accuracy. In addition, the model functioned most effectively when it split the data in a 70/30 train/test split ratio. [PERSON] and [PERSON] (2022) [18] created a hybrid autoencoder model dubbed MSCNN-LSTM-AE. This model found anomalies in network traffic by utilizing a combination of a multi-scale convolutional neural network (MSCNN) and LSTM. The MSCNN autoencoder was employed initially to evaluate the spatial characteristics of the dataset. Next, it used an LSTM-based autoencoder network to identify the temporal features of the latent space features learned from the MSCNN-AE. The authors analyzed their work with the UNSW-NB15 [19], NSL-KDD [20], and CICDDoS2019 tests. The accuracy score for their model (MSCNN-LSTM-AE) came in at 93.76%, while the recall score was 92.26%.