Detection of DDoS Attack using Machine Learning Algorithms
Detection of DDoS Attack using Machine Learning Algorithms
复制标题
使用机器学习算法检测 DDoS 攻击
DOI：
10.22214/ijraset.2024.59114
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Induja Poonati
中科院分区：
文献类型：
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作者：
Brahma Naidu Nalluri;Aditya Mandapaka;Ruzaina Suaad Mohammed;Hemanth Reddy Nagireddy;Induja Poonati
翻译后摘要：互联网的利用率在最近几十年大大增加，导致网络和网络安全的漏洞。最常见的攻击之一是分布式拒绝服务（DDoS），其中大量数据被发送到合法网站或服务器，导致延迟或拒绝合法用户的访问。单源攻击被称为拒绝服务（DoS），而来自多个源（如僵尸网络）的攻击被认为是分布式拒绝服务（DDoS）。在我们的项目中，我们采用了三种机器学习算法来识别DDoS攻击，并根据准确性指标确定了最成功的算法。我们使用标准化数据集dataset_sdn训练和测试我们的数据，并获得实验结果。在所有使用的算法中，XGBoost算法被证明是最有效的，准确率为99.9%。在预处理过程中，任何缺失的数据都将替换为列的平均值
Abstract: The utilization of the internet has greatly increased in recent decades, leading to a vulnerability in networking and cybersecurity. One of the most common resulting attacks is Distributed Denial of Service (DDoS), where overwhelming amounts of data are sent to legitimate websites or servers, causing delays or denying access to legitimate users. Single source attacks are known as denial of service (DoS), while attacks from multiple sources, such as a botnet, are considered distributed denial of service (DDoS). In our project, we employed three machine learning algorithms to identify DDoS attacks, and determined the most successful algorithm based on the accuracy metric. We trained and tested our data using the standardized dataset, dataset_sdn, and obtained experimental results. Out of all the algorithms used, the XGBoost algorithm proved to be the most effective with an accuracy of 99.9%. During preprocessing, any missing data was replaced with the column's mean value