Leveraging SDN for Efficient Anomaly Detection and Mitigation on Legacy Networks

Kostas Giotis, G. Androulidakis, B. Maglaris
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引用次数: 62

Abstract

In this paper, we investigate the applicability of Software-Defined Networking (SDN), and specifically the use of the OpenFlow protocol as a means to enhance the legacy Remote Triggered Black-Hole (RTBH) routing approach, towards Distributed Denial of Service (DDoS) attack mitigation. More specifically, we exploit the network programmability of OpenFlow to match and handle traffic on a per-flow level, in order to preserve normal operation of the victim, while pushing the mitigation process upstream towards the edge of the network. To this end, we implemented and evaluated a sketch-based anomaly detection and identification mechanism, capable of pinpointing the victim and remotely triggering the mitigation of the offending network traffic. The evaluation is based on the combination of datasets containing real DDoS attacks and normal background traffic from an operational university campus network. Our results demonstrated that the proposed approach succeeds in identifying the victim of the attack and efficiently filtering the malicious sources.
利用SDN在遗留网络上进行有效的异常检测和缓解
在本文中,我们研究了软件定义网络(SDN)的适用性,特别是使用OpenFlow协议作为增强传统远程触发黑洞(RTBH)路由方法的手段,以缓解分布式拒绝服务(DDoS)攻击。更具体地说,我们利用OpenFlow的网络可编程性,在每个流量级别上匹配和处理流量,以保持受害者的正常运行,同时将缓解过程推向网络边缘的上游。为此,我们实施并评估了一种基于草图的异常检测和识别机制,能够精确定位受害者并远程触发违规网络流量的缓解。该评估基于包含真实DDoS攻击的数据集和来自运行中的大学校园网的正常后台流量的组合。结果表明,该方法能够有效地识别攻击目标并有效地过滤恶意源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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