EfficientSTNet: A Deep Learning Approach for Multi-Class DDoS Detection

DDoS attacks are highly destructive, capable of targeting multiple devices simultaneously and posing significant threats to network systems. Therefore, it is increasingly essential to develop effective and reliable detection methods to ensure network security. Although deep learning techniques improve DDoS detection by autonomously learning feature representations, they still face challenges due to the high dimensionality and noise inherent in network traffic data, which impair computational efficiency and real-time responsiveness. Moreover, their limited adaptability to multi-class classification hinders accurate differentiation of specific attack types. This paper proposes a multi-class DDoS attack detection method based on multi-scale feature modeling—EfficientSTNet. The method employs a selective deep autoencoder for feature selection, then uses a convolutional neural network (CNN) to capture hidden spatial features in network data. Subsequently, a self-attention mechanism extracts temporal features and captures contextual dependencies among different features, improving DDoS attack detection accuracy. Finally, a fully connected layer and normalization compute the probability distribution across different classes, enabling multi-class DDoS detection. Additionally, the method integrates convolution decomposition techniques and residual network structures to accelerate model convergence and improve inference speed. The proposed EfficientSTNet model can identify different types of DDoS attacks, achieving an overall classification accuracy of 99.14% and F1-scores of 97.97% or higher across all categories, demonstrating strong practical application potential.