Regression algorithms for efficient detection and prediction of DDoS attacks

G. Dayanandam, E. Reddy, D. B. Babu
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引用次数: 5

Abstract

In the ICT era the need of depth investigation and analysis is required on network traffic. The analysis should focus on detecting DDoS attacks. In the 21st century the use of communication or transactions are completely doing through online, the political activists, and international cyber terrorists are choosing the DDoS attacks as a powerful weapon for their illegal an un ethical activities. It is impossible to the human being to identify all these unethical activities, hence the need of machine based algorithms are required. In this paper we used GLM, GBM, NN, RF regression algorithms for detection and prediction of DDoS attacks, and also proved that by using regression algorithms we observed more accurate result than using KNN SVM algorithm.
回归算法的有效检测和预测DDoS攻击
在信息通信技术时代,需要对网络流量进行深入的调查和分析。分析重点应该放在检测DDoS攻击上。在21世纪,通信和交易完全通过网络进行,政治活动家和国际网络恐怖分子将DDoS攻击作为非法和不道德活动的有力武器。人类不可能识别所有这些不道德的活动,因此需要基于机器的算法。本文使用GLM、GBM、NN、RF回归算法对DDoS攻击进行检测和预测,并证明了使用回归算法比使用KNN SVM算法观察到更准确的结果。
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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