早期DDoS攻击流量的非负增量特征检测

Ying Huang, Huizhong Sun, H. J. Chao, Xiong Chao
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引用次数: 2

摘要

分布式拒绝服务(DDoS)攻击是网络安全的主要威胁之一。本文揭示了DDoS流量吞吐量的非负和累积增量效应,这是准确区分DDoS攻击流量与正常flash人群流量的特征。基于这些特征,我们的方案可以在DDoS攻击的早期阶段进行检测。它可以有效地区分DDoS和flash人群流量,即使DDoS是潜在的。该方案检测具有在线和分布式特征的DDoS攻击。仿真结果表明了该算法的有效性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-negative Increment Feature Detection of the Traffic Throughput for Early DDoS Attack
One of the major threats to cyber security is distributed denial of service (DDoS) attacks. In this paper, we reveal the non-negative and cumulative increment effect of DDoS traffic throughput that is the feature accurately distinguished DDoS attacking traffic from normal flash crowd traffic. Our scheme can detect a DDoS attack in its early stages based on these feature. It can differentiate DDoS from flash crowd traffic effectively even if DDoS is potential. This scheme detects DDoS attacks with on-line and distributed characteristics. Simulation shows the algorithm's validity and accuracy.
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