SDN-DDoS Traffic Dataset

In cybersecurity, understanding and mitigating Distributed Denial of Service (DDoS) attacks are paramount. Although public datasets offer valuable insights, they often lack the specific characteristics or scale necessary for comprehensive research. Hence, the generation of tailored datasets is imperative for addressing the limitations of public resources. This dataset serves as a crucial resource for evaluating and refining AI algorithms, particularly in the domains of machine and deep learning. By providing a diverse array of DDoS attack traffic scenarios, researchers can develop and validate models capable of detecting and mitigating such threats in real-time. Furthermore, the dataset encompasses two distinct network topologies, each comprising 12 switches and 24 hosts, orchestrated by a Ryu controller in a Software-Defined Networking (SDN) environment. This setup enables the simulation of complex network behaviors and generation of realistic traffic patterns that are reflective of actual deployment scenarios. The dataset includes a comprehensive set of features that are essential for characterizing network traffic dynamics during DDoS attacks. These features encompass critical parameters, such as source and destination IP addresses, packet and byte counts, duration metrics, flow characteristics, protocol details, port numbers, transmission rates, delay, jitter, packet loss rates, and labeled annotations. In total, our dataset comprised an extensive collection of 1,048,575 rows, ensuring a robust and diverse sample size for rigorous analysis and experimentation. This scale facilitates the exploration of nuanced patterns and behaviors across various attack scenarios and network configurations. Furthermore, our dataset adheres to ethical guidelines and privacy standards, ensuring the anonymization of sensitive information and protection of individual privacy rights. Researchers can leverage this dataset with confidence, knowing that it upholds ethical principles while advancing the state-of-the-art DDoS detection and mitigation strategies.

在网络安全领域，理解并缓解分布式拒绝服务（Distributed Denial of Service, DDoS）攻击至关重要。尽管公开数据集能够提供富有价值的研究参考，但往往缺乏全面研究所需的特定特征与规模，因此定制化数据集的生成对于弥补公开资源的局限性极为必要。
本数据集是评估与优化人工智能算法的关键资源，尤其适用于机器学习与深度学习领域。通过提供多样化的DDoS攻击流量场景，研究人员可据此开发并验证可实时检测与缓解此类威胁的模型。
该数据集包含两种独立的网络拓扑结构，每种拓扑均由12台交换机与24台主机组成，并在软件定义网络（Software-Defined Networking, SDN）环境中由Ryu控制器进行编排。此架构能够模拟复杂的网络行为，并生成贴合实际部署场景的真实流量模式。
数据集涵盖了用于表征DDoS攻击期间网络流量动态的全套核心特征，包括源IP地址、目的IP地址、数据包与字节计数、时长指标、流特征、协议细节、端口号、传输速率、延迟、抖动、丢包率以及标注信息等关键参数。
本数据集共计包含1,048,575条记录，可为严谨的分析与实验提供充足且多样的样本规模，支持探索不同攻击场景与网络配置下的细微模式与行为特征。
本数据集遵循伦理准则与隐私标准，完成了敏感信息的匿名化处理，保障了个人隐私权益。研究人员可放心使用该数据集，在推进前沿DDoS检测与缓解技术发展的同时，恪守相关伦理原则。

搜集汇总

背景与挑战

背景概述

SDN-DDoS Traffic Dataset是一个专为DDoS攻击研究设计的综合性数据集，包含大量网络流量数据和多种攻击场景，适用于AI算法的开发和验证。数据集规模庞大，特征丰富，且严格遵循伦理和隐私标准。