Distributed Denial of Service (DDoS) attacks pose an increasingly substantial
cybersecurity threat to organizations across the globe. In this paper, we
introduce a new deep learning-based technique for detecting DDoS attacks, a
paramount cybersecurity challenge with evolving complexity and scale.
Specifically, we propose a new dual-space prototypical network that leverages a
unique dual-space loss function to enhance detection accuracy for various
attack patterns through geometric and angular similarity measures. This
approach capitalizes on the strengths of representation learning within the
latent space (a lower-dimensional representation of data that captures complex
patterns for machine learning analysis), improving the model's adaptability and
sensitivity towards varying DDoS attack vectors. Our comprehensive evaluation
spans multiple training environments, including offline training, simulated
online training, and prototypical network scenarios, to validate the model's
robustness under diverse data abundance and scarcity conditions. The Multilayer
Perceptron (MLP) with Attention, trained with our dual-space prototypical
design over a reduced training set, achieves an average accuracy of 94.85% and
an F1-Score of 94.71% across our tests, showcasing its effectiveness in dynamic
and constrained real-world scenarios.

介绍了一种基于深度学习的新技术，用于检测分布式拒绝服务攻击，在各种攻击模式中通过几何和角度相似性度量改善检测准确性。使用离线训练、模拟在线训练和原型网络场景进行了全面评估，表明该模型在不同数据充裕和稀缺条件下具有稳健性。