A DDoS Identification Method for Unbalanced Data CVWGG

As the popularity and dependence on the Internet increase, DDoS (distributed denial of service) attacks seriously threaten network security. By accurately distinguishing between different types of DDoS attacks, targeted defense strategies can be formulated, significantly improving network protection efficiency. DDoS attacks usually manifest as an abnormal increase in network traffic, and their diverse types of attacks, along with a severe data imbalance, make it difficult for traditional classification methods to effectively identify a small number of attack types. To solve this problem, this paper proposes a DDoS recognition method CVWGG (Conditional Variational Autoencoder-Wasserstein Generative Adversarial Network-gradient penalty-Gated Recurrent Unit) for unbalanced data, which generates less noisy data and high data quality compared with existing methods. CVWGG mainly includes unbalanced data processing for CVWG, feature extraction, and classification. CVWGG uses the CVAE (Conditional Variational Autoencoder) to improve the WGAN (Wasserstein Generative Adversarial Network) and introduces a GP (gradient penalty) term to design the loss function to generate balanced data, which enhances the learning ability and stability of the data. Subsequently, the GRU (Gated Recurrent Units) are used to capture the temporal features and patterns of the data. Finally, the logsoftmax function is used to differentiate DDoS attack categories. Using PyCharm and Python 3.10 for programming and evaluating performance with metrics such as accuracy and precision, the results show that the method achieved accuracy rates of 96.0% and 97.3% on two datasets, respectively. Additionally, comparison and ablation experiment results demonstrate that CVWGG effectively mitigates the imbalance between DDoS attack categories, significantly improves the classification accuracy of different types of attacks and provides a valuable reference for network security defense.