# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 1 Introduction  
Cyber Range [1] has become an essential piece of infrastructure as a significant scientific device for network security technology research, which is applied to network attack and defense drills, security product evaluation, network security personnel training, and network new technology verification. Cyber Range needs to construct NIDS to define DDoS attacks as the main target of DDoS attacks as an essential infrastructure. At present, NIDSs have evolved to leverage ML models to cope with detecting new attacks. Some researchers have proposed highly accurate DDoS detection methods based on machine learning (ML) or deep learning (DL) for SDN. For instance, [PERSON] et al. [2] proposed a method to realize UDP, ICMP, and IP spoofing attack detection by supporting the vector machine (SVM) classifier of ML. [PERSON] and [PERSON] [3] developed a flow-based intrusion detection system (IDS), which used neural network methods to detect attack flows. [PERSON] et al. [4] implemented a DDoS detection system using a stacked automatic encoder (SAE). [PERSON] et al. [5] proposed a variational automatic encoder (VAE) classifier to detect saturation attacks for the OpenFlow switch. Although these schemes can achieve high detection accuracy for DDoS attacks, the above solutions are vulnerable to adversarial attacks [6; 7; 8; 9; 10]. Attackers inject small perturbed data streams to realize adversarial attacks, which aim to reduce the accuracy of malicious attack detection for the DL algorithm. Adversarial examples refer to samples with characteristic disturbances intended to causeerror classification of deep neural networks (DNN). The attackers can still successfully execute flooding DDoS attacks by adversarial attacks under IDS-based ML or DL. The generator of the GAN model can generate many samples to construct an adversarial dataset. Using this dataset, we can avoid the challenges above in the DNN model. The GAN model is a deep generative model capable of learning the input data distribution, then producing new similar examples. A GAN consists of two models: a discriminator and a generator that are trained to compete with each other. The generator captures the training data distribution to produce fake samples resembling those from the training data. The discriminator needs to judge whether the received data are true or untruth. The generator and discriminator are constantly competing. Finally, the discriminator and a generator will reach a dynamic equilibrium. However, the added adversarial dataset expands the scale and dimension of data, and the DNN model may pay enormous computing costs to obtain suitable parameters. Obtaining or updating models is challenging and may cause overfitting, resulting in low scalability and a high error rate. We construct a DBN-LSTM model as the classifier model that can accurately detect DDoS attacks in the network and uses less time to train the model. In this paper, we propose an adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments, which can be divided into four steps: data collection, data processing, adversarial DBN-LSTM anomaly detection, and strategy distribution. The main contributions of this paper are summarized as follows:  
1. We utilize the GAN model to generate many examples and construct an adversarial dataset. The perturbed samples from the adversarial dataset are added to the training data, which already contains normal and unperturbed DDoS samples, creating augmented data samples for adversarial training. 2. We construct a DBN-LSTM model to represent the characteristics of DDoS attacks in SDN. This model extracts the time series information, completes the classification of dimension-reduction data, and significantly reduces the computational cost of large-scale data. The rest of this paper is organized as follows: Section 2 describes the background of Detecting and Defending DDoS Attacks in SDN Environments. Section 3 illustrates the structure and principles of the proposed approach.