基于ML的D3 R:使用随机森林检测DDoS

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI:10.1109/CCGridW59191.2023.00035

Anagha Ramesh, Ramza Haris, Sumedha Arora

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引用次数: 0

摘要

DDoS攻击是云服务器和网站面临的主要安全风险。为了防御这些攻击，可以采用诸如减少服务器漏洞之类的技术。本研究采用随机森林算法，通过采集网络流量数据作为输入，检测和防范DDoS攻击，增强云安全，最大限度地减少攻击损失，并分析射频的性能。结果证明了随机森林在缓解云环境中的DDoS攻击方面的有效性。

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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ML based D3 R: Detecting DDoS using Random Forest

DDoS attacks are a major security risk to cloud servers and websites. To defend against these attacks, techniques such as reducing server vulnerabilities can be employed. In this study, the Random Forest algorithm is used to detect and prevent DDoS attacks, enhancing cloud security and minimizing attack damage by collecting network traffic data as input, where the performance of RF is analyzed. Results demonstrate the effectiveness of Random Forest in mitigating DDoS attacks in cloud environments.

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来源期刊

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)

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