## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Anomaly-Based Methods
#### 3.3.7 Deep Learning-Based Detection Methods  
The deep learning structure makes use of the supremacy of both supervised and unsupervised learning with its feature extraction and classification module [163; 164]. Due to this advantage, research studies are being tailored to deep learning for DDoS attack detection in internet-enabled networks. _Attack detection in IoT and SDN using deep learning:_ [PERSON] et al. [165] proposed a deep convolutional neural network (DCNN) model for DDoS attack detection in an optical switching network. The performance of this model was compared to SVM, k-NN, and NB. The results demonstrated that DCNN outperformed SVM, k-NN, and NB, achieving 99% detection accuracy, as compared to 88%, 93%, and 79% detection accuracy for SVM, k-NN, and NB, respectively. Additionally, a misclassification rate of 1% was observed. In [166], the use of ANN with a signature-based method was investigated. The results presented showed that the combined approach has an accuracy of 99.98% with false positive rates of zero. [PERSON] et al. [167] utilized CNN and feed-forward neural networks (FNN) models for network traffic analysis and anomaly detection. When tested on the NSL-KDD dataset, the findings revealed that an accuracy of 77.84% was recorded. When compared to the other classifiers tested, including NB, RF, J48, RT, and SVM, the accuracy value was deemed to be higher. However, a detection accuracy of 77.84% is still rather low compared to other comparable research results [165; 166]. The study by the authors in [168] used LSTM for DDoS attack detection in fog computing environments. Network packets recorded at a specific time interval we employed to train the LSTM. The number of hidden layers in the LSTM was investigated for detection accuracy. The LSTM with three hidden layers and 128 units was found to be appropriate, with a detection accuracy of 98.88% when demonstrated on the ISCX 2012 dataset. The DeepDefense system proposed by [PERSON] et al. [169] leverages CNN, RNN, LSTM, and a gated recurrent unit neural network (GRUNN) to localize attacks in IoT networks. The approach was demonstrated on the ICX2012 dataset. A substantial decrease in error rate was achieved when compared to the conventional machine learning approach. With a 98% detection accuracy, the deep learning model lowers the error rate by 39.69%. In  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Method Used** & **Description** & **Dataset** & **Application Domain** & **Remarks** \\ \hline
[155] & ANN & • • ANN for detecting HF, DR, and VN attacks in RPL-based IoT networks. & \ \begin{tabular}{c} Simulated \\ \end{tabular}  & \begin{tabular}{c} RPL-based \\ IoT \\ \end{tabular}  & • • Attacks are localized with 100\% accuracy. \\ \hline
[157] & RF, NB, J48 & • Evaluate the performance of three classifiers for attack detection. & \ \begin{tabular}{c} Simulated \\ \end{tabular}  & \begin{tabular}{c} RPL-based \\ IoT \\ \end{tabular}  & • • Attacks are localized with 100\% accuracy. \\ \hline
[160] & \begin{tabular}{c} DT, J48, MLP \\ + BP \\ \end{tabular}  & • • Simulate IFA to gather attack features. & \begin{tabular}{c} \(\bullet\) \\ \end{tabular}  & \begin{tabular}{c} \(\bullet\) \\ \end{tabular}  & \begin{tabular}{c} \(\bullet\) \\ \end{tabular}  & \ \begin{tabular}{c} \(\bullet\) \\ \end{tabular}  & \begin{tabular}{c} \(\bullet\) \\ \end{tabular}  \\ \hline
[162] & RF & •  
research by [PERSON] et al. [170], the utilization of two methodologies for attack detection in an IoT network was examined independently. The first method makes use of a hybrid intrusion detection system, while the second approach uses an LSTM deep learning model. The applicability of these approaches was demonstrated on the CICDDoS2019 dataset. The two methods had a detection accuracy of 91.9% for both DDoS and DoS attacks. In this study, a few cases of false alarms were recorded. [PERSON] et al.