TorusSDN-DDoS Defense Dataset

The "TorusSDN-DDoS Defense Dataset" is meticulously crafted to tackle the intricate task of identifying Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) setups, with a particular focus on the Torus network topology. It encompasses a wide array of network traffic data, encompassing both normal operations and malicious DDoS traffic patterns, making it an indispensable tool for the creation, evaluation, and refinement of sophisticated DDoS detection methodologies.

Structured to complement the capabilities of the Mininet emulator for SDN scenarios, the dataset provides extensive details on network dynamics, including packet rates, sizes, and types, along with flow counts and protocol specifications. Each record within the dataset is distinctly labeled to indicate its nature as either "normal" traffic or a "DDoS" attack, thereby enabling the application of supervised learning techniques and facilitating thorough analytical research.

Additionally, the dataset is feature by its inclusion of 35 primary network are instrumental in the identification of DDoS attacks, thereby offering a comprehensive resource for cybersecurity professionals and researchers aiming to enhance SDN defenses against DDoS threats.

《TorusSDN-DDoS 防御数据集》系精心打造，旨在应对软件定义网络（SDN）环境中分布式拒绝服务（DDoS）攻击的复杂任务，尤其关注环状网络拓扑。该数据集涵盖了广泛的网络流量数据，包括正常操作和恶意 DDoS 流量模式，因此成为创建、评估和优化高级 DDoS 检测方法的不可或缺工具。结构上与 Mininet 模拟器在 SDN 场景下的功能相辅相成，该数据集提供了关于网络动态的详细信息，包括数据包速率、大小和类型，以及流量计数和协议规范。数据集中的每条记录均明确标注为“正常”流量或“DDoS”攻击，从而便于应用监督学习技术并促进深入的分析研究。此外，该数据集还包含35个关键网络特征，这些特征对于识别 DDoS 攻击至关重要，因此为致力于提升 SDN 防御 DDoS 威胁的网络安全专业人士和研究人员提供了一个全面的研究资源。