DDoS攻击单参数识别方法

J. Smieško, J. Uramová
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引用次数: 2

摘要

在本文中,我们处理使用单参数机器学习方法来识别DDoS攻击。同时,我们希望在我们的研究工程项目“应用网络工程”的课程中展示网络安全研究的实施情况。我们重点研究了一阶自回归分析的自回归系数和赫斯特系数,它表示观测流的自相似程度。我们测试了系数在模拟数据上及时检测DDoS攻击期间IP流结构变化的能力,随后在几个记录的真实DDoS攻击上进行了测试,这些攻击由我们的学生进行了预处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
One-parameter Methods for Recognizing DDoS Attacks
In this article we deal with the use of one-parameter machine learning methods for the recognition of DDoS attacks. At the same time, we want to present the implementation of research focused on cybersecurity in the curriculum of our study engineering program Applied Network Engineering. We focused on the autoregressive coefficient of the first order autoregressive analysis and on the Hurst coefficient, which expresses the degree of self-similarity of the observed flow. We tested the ability of the coefficients to detect a change in the structure of the IP flow during a DDoS attack in time on simulated data and subsequently on several recorded real DDoS attacks which were preprocessed by our students.
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