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Regression algorithms for efficient detection and prediction of DDoS attacks

机译:用于有效检测和预测DDoS攻击的回归算法

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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 21stcentury 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.
机译:在ICT时代,需要对网络流量进行深入调查和分析。分析应集中在检测DDoS攻击上。在21 st 世纪以来,通信或交易的使用完全通过在线进行,政治活动家和国际网络恐怖分子都选择DDoS攻击作为其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。在本文中,我们将GLM,GBM,NN,RF回归算法用于DDoS攻击的检测和预测,并证明通过使用回归算法,与使用KNN SVM算法相比,观察到的结果更准确。

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