基于双空间原型网络的方法重新定义DDoS攻击检测
Redefining DDoS Attack Detection Using A Dual-Space Prototypical Network-Based Approach
AI总结：
针对DDoS攻击检测的挑战，提出基于双空间损失函数的双空间原型网络方法，结合隐空间表示学习提升检测性能，在多场景下验证了高准确率与鲁棒性。
摘要：
分布式拒绝服务（Distributed Denial of Service，DDoS）攻击对全球各类组织构成的网络安全威胁日益严峻。本文提出了一种新的基于深度学习的DDoS攻击检测技术，以应对这一复杂度和规模不断演变的核心网络安全挑战。具体而言，我们提出了一种新型双空间原型网络，该网络借助独特的双空间损失函数，通过几何相似度与角度相似度度量，提升对多种攻击模式的检测准确率。该方法充分利用隐空间（即数据的低维表示，可捕获复杂模式以供机器学习分析）内的表示学习优势，增强了模型对不同DDoS攻击载体的适应性与敏感度。我们开展了涵盖多种训练环境的综合评估，包括离线训练、模拟在线训练以及原型网络场景，以验证模型在数据充足和数据稀缺等不同条件下的鲁棒性。采用我们的双空间原型设计、在缩减训练集上训练的带注意力机制的多层感知器（Multilayer Perceptron, MLP），在所有测试中取得了94.85%的平均准确率和94.71%的F1分数，证明了其在动态、受限的真实场景中的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
