Uses geometric and angular similarity in a dual-space prototypical network to detect known and novel DDoS attack patterns with limited training data.
Abstract
Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. Thus, this paper proposes an innovative deep learning-based technique for detecting DDoS attacks, a paramount cybersecurity challenge with evolving complexity and scale. We propose a novel Dual-space Prototypical network that leverages a unique dual-space loss function to enhance detection accuracy for both known and novel attack patterns through geometric and angular similarity measures. This approach capitalizes on the strengths of representation learning within the latent space, 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 MLP with Attention, trained with our Dual-space Prototypical design over a reduced training set, demonstrates outstanding performance, achieving 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.