# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method
### Proposed Hybrid Deep Learning Approach  
In our research, we propose a hybrid deep learning model for detecting distributed denial of service (DDoS) attacks in software-defined networking (SDN). The choice of deep learning algorithms depends on the specific characteristics of the problem being addressed. In the case of detecting DDoS attacks in SDN networks, a combination of a 1D convolutional neural network (CNN), a gated recurrent unit (GRU), and a dense neural network (DNN) was selected because they each have strengths that complement each other in handling the complexities of the problem. The 1D CNN is a powerful tool for detecting patterns in sequential data, such as network traffic. It is able to automatically extract relevant features from the input data, such as packet size, frequency, and direction, and capture both short-term and long-term patterns. This is especially useful for detecting low-rate DDoS attacks, which can be difficult to detect using traditional methods. The GRU is a type of recurrent neural network (RNN) that is well suited for handling sequential data with long-term dependencies. It is able to learn and remember patterns over long periods, which is important for detecting subtle changes in network traffic that may be indicative of an attack. Finally, the DNN is a versatile algorithm that is able to learn complex mappings between input and output data. It is able to handle a wide range of input data types and can be used to model complex relationships between different input features. Other deep learning algorithms could also be used, but the combination of 1D CNN, GRU, and DNN was found to be particularly effective in detecting and defending against DDoS attacks in SDN networks. The selection of algorithms should be based on the specific problem being addressed and the characteristics of the data being analyzed. Our approach was able to effectively detect both low-rate and volumetric DDoS attacks in SDN networks. This was achieved through the use of a combination of deep learning algorithms, including a 1D convolutional neural network (CNN), a gated recurrent unit (GRU), and a dense neural network (DNN). Our experience and results have shown that this combination of algorithms is particularly effective in addressing the unique challenges posed by DDoS attacks in SDN networks. By considering both types of attacks, our approach was able to provide a more comprehensive and accurate analysis of the security challenges facing SDN networks. Additionally, our hybrid deep learning approach, which combined multiple algorithms and datasets, further improved the accuracy and efficiency of our analysis. Overall, our approach represents a significant contribution to the field of DDoS attack detection and defense in SDN networksThe 1D CNN is responsible for extracting features \(x_{i}\) from the input network traffic data, which is typically in the form of time series data. The features \(x_{i}\) are extracted using the following convolutional operation:  
\[x_{i}=f(W\times x_{i-1}+b)\]  
where \(W\) is the weight matrix, and \(b\) is the bias term. The GRU is a type of recurrent neural network (RNN) that is able to process sequences of data and capture long-term dependencies, making it well suited for analyzing time series data. The GRU computes hidden states \(h_{i}\) using the following equation:  
\[h_{i}=(1-z_{i})\times h_{i-1}+z_{i}\times c_{i}\]  
where \(z_{i}\) and \(c_{i}\) are gate variables computed using sigmoid and tanh activation functions, respectively. The DNN is a fully-connected neural network that is used to classify the input data as either normal or attack traffic based on the features extracted by the 1D CNN and the temporal dependencies captured by the GRU. The output \(y_{i}\) of the DNN is computed using the following equation:  
\[y_{i}=f(W_{out}\times h_{i}+b_{out})\]  
where \(W_{out}\) and \(b_{out}\) are the weight matrix and bias term for the output layer.