Detection of DDoS Attacks Using Machine Learning Classification Algorithms

Q1 Mathematics
K. Dasari, N. Devarakonda
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引用次数: 2

Abstract

The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accurate than traditional classification methods. This is a quantitative research applies Logistic Regression, Decision Tree, Random Forest, Ada Boost, Gradient Boost, KNN, and Naive Bayes classification algorithms to detect DDoS attacks on the CIC-DDoS2019 data set, which contains eleven different DDoS attacks each containing 87 features. In addition, evaluated classifiers’ performances in terms of evaluation metrics. Experimental results show that AdaBoost and Gradient Boost algorithms give the best classification results, Logistic Regression, KNN, and Naive Bayes give good classification results, Decision Tree and Random Forest produce poor classification results.
使用机器学习分类算法检测DDoS攻击
互联网是当今世界最重要的交流工具。因此,网络攻击越来越频繁,后果的严重性也在上升。分布式拒绝服务是最有效、代价最高的五大网络攻击之一。分布式拒绝服务(DDoS)是一种阻止合法用户访问网络系统资源的网络攻击。为了最大限度地减少重大损失,快速准确的DDoS攻击检测技术至关重要。为了对目标类进行分类,机器学习分类算法比传统的分类方法更快、更准确。这是一项定量研究,应用逻辑回归、决策树、随机森林、Ada-Boost、梯度Boost、KNN和Naive Bayes分类算法来检测CIC-DDoS2019数据集上的DDoS攻击,该数据集包含11种不同的DDoS攻击——每个攻击包含87个特征。此外,还根据评价指标对分类器的性能进行了评价。实验结果表明,AdaBoost和Gradient Boost算法的分类效果最好,Logistic回归、KNN和Naive Bayes算法的分类结果较好,决策树和随机森林算法的分类性能较差。
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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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
33
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