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SDN中DDoS攻击细粒度检测技术探讨
Fine-grained Detection Technique of DDoS Attack in SDN
【摘要】 在SDN中对DDoS攻击进行检测的关键在于轻量化与实时性的保障,但现阶段DDoS检测方法多以特定攻击手段为关注点,不能在多攻击场景下有效保持轻量化及实时性。针对这一问题,本文提出一种基于CNN的细粒度检测技术,力求做到对轻量化和实时性的兼顾,实现大量场景的快速检测。
【Abstract】 The key to detect DDoS attacks in SDN is the guarantee of lightweight and real-time. However, at this stage, DDoS detection methods mostly focus on specific attack means, and can not effectively maintain lightweight and real-time in multi-attack scenarios. In view of this problem, this paper proposes a fine-grained detection technology based on CNN, striving to achieve both lightweight and real-time, and realize the rapid detection of a large number of scenarios.
【关键词】 软件定义网络;
DDoS攻击;
卷积神经网络;
细粒度检测;
【Key words】 software-defined network; DDoS attack; convolutional neural network; fine-grained detection;
【Key words】 software-defined network; DDoS attack; convolutional neural network; fine-grained detection;
【基金】 2022年中国高校产学研创新基金—新一代信息技术创新项目“SDN中混合防御DDoS攻击检测技术研究”(2022 IT 078);教育部产学合作协同育人项目“基于大数据分析的安全威胁态势感知平台应用研究”(230811452507212)
- 【文献出处】 软件 ,Software , 编辑部邮箱 ,2024年10期
- 【分类号】TP393.08
- 【下载频次】11