SDN中SYN泛洪攻击的检测与缓解

Nan Haymarn Oo, A. Maw
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引用次数: 0

摘要

. 软件定义网络将网络体系结构分为逻辑控制层和数据转发层,以提供较高的灵活性、敏捷性和安全性。虽然它从控制器上管理整个网络,易于编程,但SDN架构仍然存在许多安全问题。通过DDoS攻击,攻击者的目标可以是SDN的各个层。确定检测和缓解攻击的阈值是最重要的问题之一。现有研究强调在SDN基础设施中使用各种机制检测DDoS攻击。本文提供了一种简单的机制来检测和缓解常见类型的DDoS攻击,SYN泛洪攻击,通过sFlow分析仪使用自适应阈值算法计算动态阈值。它使用自己生成的网络流量,包括正常流量和攻击流量,并展示了计算的动态阈值如何适应传入流量。通过检测率、虚警率、误报率、准确率等指标对检测和缓解机制的性能进行评估,以证明系统能够及时检测和合理缓解DDoS攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SYN Flooding Attack Detection and Mitigation in SDN
. Software-defined networking separates network architecture into logical control layer and data forwarding layer with the aim of providing high flexibility, agility, and security. Although it manages the whole network from the controller with the ease of programmability, many security issues still exist in SDN architecture. Attacker's target can be at the various layers of SDN by DDoS attack. Defining threshold in detection and mitigation of the attack is one of the most important issues. Existing researches emphasize the detection of DDoS attack with various mechanisms in SDN infrastructure. This paper provides a simple mechanism for both detection and mitigation of common type of DDoS attack, SYN flooding attack via sFlow analyzer with dynamic threshold calculated by using adaptive threshold algorithm. It uses own generated network traffic consisting both normal and attack traffic and shows that how the calculated dynamic threshold adapts the incoming traffic. It also evaluates the performance of the detection and mitigation mechanism by detection rate, false alarm rate, false negative rate, and accuracy in order to prove our proposed system can timely detect and reasonably mitigate DDoS attack.
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