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在基于Snort的CDX 3平台中实验检测分布式拒绝服务攻击

Chin-Ling Chen¹, Jian Lin Lai¹

¹Department of Information Management, National Pingtung University, Pingtung 900, Taiwan.

Sensors (Basel, Switzerland)

|July 14, 2023

概括

本研究介绍了一种高效的Poisson分布模型,用于在没有机器学习培训的情况下检测分布式拒绝服务 (DDoS) 攻击. 这种新的方法为现实世界的互联网和云安全提供了卓越的性能.

科学领域:

计算机科学计算机科学
网络安全网络安全
网络安全网络安全

背景情况:

分布式拒绝服务 (DDoS) 攻击对互联网和云基础设施安全构成重大威胁.
现有的检测方法,特别是基于机器学习 (ML) 的算法,通常需要大量的培训时间和计算资源.

研究的目的:

使用Poisson分布模型开发和验证一种高效的DDoS攻击检测方法.
通过消除培训时间的需求,为基于机器学习的方法提供一个计算高效的替代方案.

主要方法:

用Poisson分布模型来检测DDoS攻击,其计算复杂度为0n.
在预处理过程中,使用基于时间段颗粒度的Poisson分布来建模合法的流量.
该方法在CDX 3.0平台上使用模拟各种DDoS攻击场景的四台虚拟机进行了验证.
分析了七种不同的DDoS攻击方法.

主要成果:

拟议的Poisson模型实现了高效的DDoS攻击检测,复杂度为O.
与现有的检测技术相比,该方法显示出更高的性能.
验证证实了该模型在识别和分析各种DDoS攻击类型方面的有效性.

结论:

波桑分布模型为现实世界DDoS攻击检测提供了有效和高效的解决方案.
这种方法通过消除ML算法所需的训练阶段,提供了显著的优势.
该研究强调了这种方法在加强互联网和云安全方面对DDoS威胁的潜力.

关键词:

这是一种DDoS攻击.云计算是云计算中的一个.互联网安全互联网安全侵入检测系统的入侵检测系统

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