A DDoS Attack Detection using PCA Dimensionality Reduction and Support Vector Machine

Bhargavi Goparaju, Bandla Sreenivasa Rao
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引用次数: 1

Abstract

Distributed denial-of-service attack (DDoS) is one of the most frequently occurring network attacks. Because of rapid growth in the communication and computer technology, the DDoS attacks became severe. So, it is essential to research the detection of a DDoS attack. There are different modes of DDoS attacks because of which a single method cannot provide good security. To overcome this, a DDoS attack detection technique is presented in this paper using machine learning algorithm. The proposed method has two phases, dimensionality reduction and model training for attack detection. The first phase identifies important components from the large proportion of the internet data. These extracted components are used as machine learning’s input features in the phase of model detection. Support Vector Machine (SVM) algorithm is used to train the features and learn the model. The experimental results shows that the proposed method detects DDoS attacks with good accuracy.
基于PCA降维和支持向量机的DDoS攻击检测
分布式拒绝服务攻击(DDoS)是最常见的网络攻击之一。随着通信技术和计算机技术的飞速发展,DDoS攻击日益严重。因此,研究DDoS攻击的检测是十分必要的。DDoS攻击有多种模式,单一的攻击方式无法提供良好的安全性。为了克服这一问题,本文提出了一种基于机器学习算法的DDoS攻击检测技术。提出的攻击检测方法分为降维和模型训练两个阶段。第一阶段从大量互联网数据中识别出重要的组成部分。这些提取的成分被用作模型检测阶段机器学习的输入特征。使用支持向量机(SVM)算法训练特征并学习模型。实验结果表明,该方法检测DDoS攻击的准确率较高。
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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